Technology at Home: Implications for Children - Epistemic Games Group

Journal of Special Education Technology

Editor J. Emmett Gardner, University of Oklahoma Editorial Assistant Mark Yapelli, University of Oklahoma Associate Editors Ashley Skylar, California State University–Northridge—Assistive Technology Dave Edyburn, University of Wisconsin–Milwaukee—Research and Practice Barbara L. Ludlow, West Virginia University, and John D. Foshay, Central Connecticut State University—Book and Software Review Cheryl Wissick, University of South Carolina, and Windy Schweder, University of South Carolina–Aiken—Content-Area Applications H. Michael Crowson, University of Oklahoma—Statistical Design and Analysis

Editorial Review Board Cindy Anderson

Gail Fitzgerald

John Langone

Kimberly J. Paulsen

Lynn Anderson-Inman

John Foshay

Central Connecticut State University

Rena Lewis

Torn Pierce

University of Oregon

San Diego State University

University of Nevada–Las Vegas

Tamarah Ashton

Marilyn Friend

Carl Liaupsin

Marshall Raskind

Roosevelt University

University of Missouri–Columbia

University of Georgia

California State University– Northridge

University of North Carolina– Greensboro

Kevin M. Ayres

Douglas Fuchs

Joan Lieber

Vanderbilt University

University of Maryland

Lynn Fuchs

Charles MacArthur


University of Delaware

J. Emmett Gardner

David Majsterek

Michael Gerber

Mary Male

University of Georgia

Christine Bahr

St. Mary-of-the-Woods College

James D. Basham

University of Cincinnati

Margaret Bausch

University of Kentucky

University of Oklahoma University of California–Santa Barbara

University of Arizona

Central Washington University San Jose State University

Susan Mistrett

University of Wisconsin–Madison

Louisiana Tech University

University at Buffalo

Emily C. Bouck

Leah Herner

Joel Mittler

Purdue University

The Ohio State University

CW Post University

Amanda Boutot

Richard Howell

Betty Nelson

Tara Jeffs

Angela Notari-Syverson

Lisa Bowman

Juniper Gardens

Monica Brown

New Mexico State University

John Castellani

East Carolina University

Martin Kaufman

University of Oregon

Elizabeth Lahm

University of Alabama–Birmingham Washington Research Institute

Steve Nourse

Central Washington University

Theresa A. Ochoa

Johns Hopkins University

Wisconsin Assistive Technology Initiative

Sharon F. Cramer

Paula Lancaster

Cynthia Okolo

Grand Valley State University

Michigan State University

Sean Lancaster

Phil Parette

Buffalo State College

David L. Edyburn

University of Wisconsin–Milwaukee

Grand Valley State University

David Rose

Center for Applied Special Technology

Ralf W. Schlosser

Northeastern University

Windy Schweder

University of South Carolina–Aiken

Ashley Skylar

Sean Smith

Brenda Heiman

University of New Mexico

Macon State College

California State University– Northridge

Brian Bottge

DePaul University

Laila J. Richman

Institute of Education Sciences

Ted Hasselbring

University of Kentucky

Schwab Learning

David Malouf

Regina Blair

University of Texas–Austin


Indiana University

Illinois State University

University of Kansas

Steven B. Smith

Northern Kentucky University

Joseph Stowitschek

University of Washington

Teresa Taber Doughty Purdue University

Matthew Tincani

University of Nevada

Renee K. Van Norman

University of Nevada–Las Vegas

Mike Wehmeyer

University of Kansas

Cheryl Wissick

University of South Carolina


Table of Contents ®® The Future is Now: Application and Innovation of Technology

in Special Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Tracy Gray and Heidi Silver-Pacuilla

®® Defining Quality Indicators for Special Education Technology Research . . . . . . . . 3 Russell Gersten and Dave Edyburn

®® Research About Assistive Technology: 2000-2006.

What Have We Learned? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Cynthia M. Okolo and Emily C. Bouck

®® Speech Technology and Its Potential for Special Education . . . . . . . . . . . . . . . . . . 35 Yong Zhao

®® Technology at Home: Implications for Children

with Disabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Emily C. Bouck, Cynthia M. Okolo, and Carrie Anna Courtad

®® Epistemic Games as Career Preparatory Experiences

for Students with Disabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 David Williamson Shaffer

JSET Volume 22, Number 3

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Editorial Policy and Goals

The Journal of Special Education Technology (JSET) is a refereed professional journal that presents up-to-date information and opinions about issues, research, policy, and practice related to the use of technology in the field of special education. JSET supports the publication of research and development activities, provides technological information and resources, and presents information and discussion concerning important issues in the field of special education technology to scholars, teacher educators, and practitioners. The mission of JSET is: “to provide a vehicle for the proliferation of information, research, and reports of innovative practices regarding the application of educational technology toward the development and education of exceptional children.” JSET is a publication of the Technology and Media (TAM) Division of the Council for Exceptional Children.

• • • • • • •

The goals of TAM include:

•

• Promoting collaboration among educators and

others interested in using technology and media

to assist individuals with exceptional educational needs. Encouraging the development of new applications, technologies, and media that can benefit individuals with exceptionalities. Disseminating relevant and timely information through professional meetings, training programs, and publications. Coordinating the activities of educational and governmental agencies, business, and industry. Developing and advancing appropriate technical standards. Providing technical assistance, in-service, and preservice education on the uses of technology. Monitoring and disseminating relevant research. Advocating for funds and policies that support the availability and effective use of technology in this field. Supporting the activities, policies, and procedures of CEC and other CEC divisions.

TAM Board Members Brenda Heiman, President

Cheryl Wissick, Treasurer

Cynthia Warger, Publications Chair

Tara Jeffs, President Elect

Joel Mittler, CAN Coordinator

John Lowdermilk, Professional Development Chair

Betty Nelson, Vice President

James Gardner, Awards Chair

Joy Zabala, Past President

Diane Painter, Member-at-Large

Jennie I. Schaff, Secretary

Deborah Newton, Member-at-Large

Louisiana Tech University East Carolina University

University of Alabama—Birmingham Assistive Technology and Leadership Nazareth College

University of South Carolina Long Island University

University of Oklahoma Hood University

Southern Connecticut State University

Warger, Eavy & Associates

University of Texas—Pan Am

James Gardner, Journal Editor University of Oklahoma

Susan Mistrett, Webmaster University at Buffalo

Joy Zabala, Newsletter Editor

Assistive Technology and Leadership

Subscriptions and Membership in TAM The Journal of Special Education Technology (JSET) is a traditional print-on-paper publication (4 issues per year) that is sent to subscribers and members of the Technology and Media Division of the Council for Exceptional Children (TAM). Subscriptions to ]SET are available without membership in TAM at the following rates: Individual domestic mail: $55 per year Institutional or foreign mail: $109 per year All inquiries concerning subscriptions should be sent to: Exceptional Innovations, Inc. ATT: JSET P.O. Box 3853 Reston, VA 20195 703-709-0136 [email protected]

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Membership inquiries should be directed to the address below or a membership brochure may be found online at the TAM Web site at: http://www.tamcec.org. The Council for Exceptional Children 1110 North Glebe Road Suite 300 Arlington, VA, 22201-5704 703-620-3663 or 1-888-CEC-SPED toll free TTY: 703-264-9446 fax: 703-264-9494 http://www.cec.sped.org



The Future is Now: Application and Innovation of Technology in Special Education Tracy Gray Heidi Silver-Pacuilla National Center for Technology Innovation American Institutes for Research

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n this topical issue, appropriately titled The Future is Now: Application and Innovation of Technology in Special Education, we argue that given the pace of innovation in technology, looking to the future is key to understanding the potential of special education technology. In our 2005 National Center For Technology Innovation (NCTI) report, Moving Toward Solutions: Assistive and Learning Technologies for All Students (NCTI), we noted that the field is at a “tipping point,” a convergence of forces and opportunities. It is an opportune time when educators, parents, and policymakers are seeking solutions to ensure that all students have the opportunity to reach their learning potential. First, it will be useful to consider adopting Malcolm Gladwell’s (Gladwell, 2003) theory of the tipping point and use it to describe the “we” that makes up our field. Gladwell’s analysis hinges upon the importance of two classes of individuals: the Mavens and the Connectors. Mavens are individuals who know everything about something. They are passionate about their subject and learn voraciously and share just as generously. If you have ever had the opportunity to witness some of the authors of this edition discuss the potential for technology in learning, Dave Edyburn, Russell Gersten, Cynthia Okolo, David Shaffer, or Yong Zhao, you know they are the classic Mavens for the field of assistive and learning technologies. They inspire even the most tech-aversive audience member with stories, demonstrations, and sheer exuberance. With technology applications, features, and capabilities exploding these past few years, it is difficult to stay up-todate and still have energy left over to engage innovation with imagination. We need our Mavens to help us make sense of the changes and suggest new directions. Connectors, on the other hand, are collectors. They collect people and find ways to connect them. They are ag-


gressive networkers whose passion is to share the special qualities of the contacts—the people and products they have collected. Those of us who have had the opportunity to work with Jane Hauser and Dave Malouf at the U. S. Department of Education know that they have established an extraordinary network that connects researchers and innovators. The mandate of NCTI is to sustain and grow this network to enhance the quality and implementation of technology tools and solutions to improve educational outcomes for all students. Most of us fall somewhere in between, finding ourselves influenced in some way by the Mavens and the Connectors. It is likely that we all know at least one of each; in fact, a sociogram would indicated that we probably know the same Mavens and Connectors for the field of special education technology. We read their articles, follow their online postings, and attend their sessions at conferences. Furthermore, we need to realize that WE are the field! Our field is all the educators, researchers, innovators, vendors, policymakers, and consumers who understand and work toward the integration of technology as part of the learning experience for all students. We belong to a network informed and enhanced by the Mavens and Connectors who are passionate and generous with their knowledge and energy. We connect other networks to the assistive and learning technology network through our diverse interests and connections, thus enriching the whole. These connections and the momentum they carry are critical if we are to tip the assistive and learning technology field in the direction of being more visible and integral to the learning experience of all students, particularly those with special needs. Without our active engagement, the field risks tipping in the opposite direction—toward becoming obsolete and out of step with mainstream school reform efforts.

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Journal of Special Education Technology This topical issue of The Journal of Special Education Technology is an opportunity to report on the leading edge of innovation and how it contributes to the convergence of forces at the tipping point. Russell Gersten and Dave Edyburn make the case that the field is not well served by its current research base: “Historically, the use of technology in special education has been advanced on the basis of marketplace innovations and federal policy initiatives rather than on the application of a compelling research base.” To strengthen and guide future research, they present a detailed rubric of quality indicators for special education technology that will engender discussion about research designs, methods, and dissemination. This conversation is critical to keeping the assistive and learning technology field current with other educational research reforms and trends. The article by Cynthia Okolo and Emily Bouck provides a detailed view of the existing research base on interactive technologies used with students with disabilities. Their review of empirical, peer-reviewed articles illuminates productive sources of newly published research, such as JSET, and the many gaps in our knowledge about promising practices. Their findings promote a coherent research-based practice and call us to action by pointing out that a wealth of topics await the attention of assistive technology researchers. The article by Yong Zhao takes a wide-lens view of innovations in speech technology, noting that this trend is not tied to education: “The effectiveness of speech technology for students with disabilities depends as much on its own capacity as on how it is used by educators. Educators in special education need to reinterpret this capacity in the context of the learning needs of students in order to translate technological capacities into learning solutions.” He challenges us to adapt the technology to learning needs and realistic settings to create solutions for students. Turning to young children, Emily Bouck, Cynthia Okolo, and Carrie Anna Courtad’s article explores the research on engaging children with disabilities through the use of smart toys and computers in the home and other outof-school settings. Despite the years of service to young children with disabilities through early intervention efforts, the research base remains slim. However, a glimmer of change is offered by the authors as they reflect on the potential application of widespread consumer elec2

tronics: “These common devices provide students with and without disabilities access to educational, organizational, social, and recreational opportunities, presented in a ‘socially-acceptable’ and nonstigmatizing manner.” Understanding how children are using and learning from consumer learning technologies is critical to maximizing their potential for students with disabilities. Finally, creating new solutions with innovative technology is also the challenge offered by David Williamson Shaffer. He describes epistemic games that “provide realistic images of professional life, but do so in a way that is designed to develop the knowledge, skills, and values that are also essential components of academic success. They are about ways of thinking, rather than specific vocational skills.” Offering this kind of experience to students in their transition planning is an idea worthy of further discussion as to how such experiences could broaden students’ understanding of possible careers and options for their future. In conclusion, to move our field to a tipping point that involves effective implementation of assistive and learning technologies, we must coordinate our research efforts to provide evidence on what works for whom, where, and when; foster the implementation of innovative uses of technology; and train a new generation of practitioners and leaders to understand the potential of technology to improve the system and outcomes for all students.

References National Center For Technology Innovation (2005). Moving Toward Solutions: Assistive and Learning Technologies for All Students. Washington, DC: American Institutes for Research Gladwell, M. (2003). The Tipping Point: How little things can make a big difference. Boston, MA: Little, Brown and Company.

Author Notes Tracy Gray is the Director, and Heidi Silver-Pacuilla is the Deputy Director, National Center for Technology Innovation, American Institutes for Research. The National Center for Technology Innovation is a five year (2006-2011) grant funded by the U.S. Department of Education, Office of Special Education Programs (OSEP). Correspondence should be addressed to Tracy Gray, National Center for Technology Innovation, American Institutes for Research, 1000 Thomas Jefferson Street, NW, Washington, D.C. 20007. Email to [email protected] JSET Volume 22, Number 3


Defining Quality Indicators for Special Education Technology Research Russell Gersten Instructional Research Group University of Oregon Dave Edyburn University of Wisconsin-Milwaukee Historically, the use of technology in special education has been advanced on the basis of marketplace innovations and federal policy initiatives rather than on a compelling research base. This article presents a set of quality indicators that will guide efforts to enhance that base. Thirty quality indicators, organized into eight areas (conceptualization of the research study, full disclosure, sample selection, description of participants, implementation of the intervention, outcome measures, data analysis, and publication and dissemination), are briefly described and three variations of each (unacceptable, essential, and desirable) are highlighted. Particular attention is paid to the distinct methodological design issues associated with technology research and development. Recommendations are made for the use of these quality indicators to enhance the evidence base for the field.

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istorically, the use of technology in special education has been advanced on the basis of marketplace innovations and federal policy initiatives rather than a compelling research base. The field has been characterized by a long-standing commitment to explore the applications of new technologies for persons with disabilities (Blackhurst, 2005). Demonstrating the use of innovative technologies on an individual case basis (Office of Technology Assessment, 1982), technology advocates have utilized federal policy initiatives (e.g., the Technology-Related Assistance for Individuals with Disabilities Act of 1988 and the 1997 Individuals with Disabilities Education Improvement Act reauthorization) as a scaling-up strategy to capture the potential of technology for persons with disabilities (Edyburn, 2005). While research is a critical component of the field of special education technology, it has not been a major driver in the advancement of the profession. Over the years, various observers have commented on the condition of the extant research base for informing the design and


utilization of technology in special education. The following paragraphs summarize the range of perspectives represented. Recognizing the disconnect between development cycles in the marketplace and timelines associated with research and publication, Hannaford (1993) was concerned about the increased prevalence of unsubstantiated claims: Much of what is presented as being known about the use of computers with exceptional persons is actually what is believed, felt, or hoped. While there is an increasing amount of research and evaluation support associated with various uses of the technology, there is still relatively little empirical support for many statements found in the popular literature. (p. 12)

Cognizant of the youth of the discipline and the lack of tools for accessing the emerging research base, Edyburn created a literature synthesis methodology known as the “comprehensive one-year review.” His annual reviews of the literature between 1999 and 2003 reveal that the knowledge base has been growing at a rate of over 200

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Journal of Special Education Technology articles per year but is characterized by issues of practice (e.g., product features, how-to, troubleshooting) rather than issues of efficacy by a ratio of nearly 2:1 (Edyburn, 2004). Special education technology has a myopic focus on what is new in the field and is, therefore, subject to the same criticisms as the field of educational technology in terms of advances being advocated on the basis of ideology rather than research validation of what works (Willis, 2003). The lack of validated measurement tools has impaired the field’s ability to measure the outcomes of special education technology. For example, it was not until 1996 that the literature began featuring calls to systematically measure the outcomes of assistive technology (DeRuyter, 1997; Smith, 1996). Subsequently, a national survey revealed that few standardized instruments were available for measuring outcomes, and that most outcome assessments were locally developed with unknown technical adequacy (Rehabilitation Engineering and Assistive Technology of North America, 1998a, 1998b, 1998c). In hindsight, formal measures were not collected on the outcomes of assistive technology because outcomes were self-evident. That is, a person with a disability exhibited a performance problem and sought assistance from an assistive technology professional, who provided an intervention (device and service) that enhanced performance to a level higher than was found at the time of the initial referral. As a result, the outcome and benefit of assistive technology was obvious. Given the lack of formal measures for measuring assistive technology outcomes, consumer satisfaction has served for many years as a proxy for outcome. While concerns have been raised about the quality and use of the special education technology research base (or lack thereof ), several indicators profile the growth and impact of the knowledge base in the past 25 years. For example, a number of research syntheses (Okolo, Bahr, & Rieth, 1988; Woodward & Rieth, 1997) have had a major impact on the profession by informing the discipline about effective practice and inspired new research agendas. In a similar vein, the U. S. Department of Education’s Office of Special Education Programs (OSEP) has documented the array of benefits that resulted from the federal investment in special education technology research and development (Fein, 1996; Hauser & Malouf,

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1996). Further, the recent publication of the profession’s first research handbook (Edyburn, Higgins, & Boone, 2005) marks a developmental milestone in synthesizing and analyzing the special education technology research knowledge base. Federal policy has challenged the educational community to generate research that meets more rigorous standards of proof (Mosteller & Boruch, 2002; Shavelson & Towne, 2002; Whitehurst, 2003). Specifically the special education community has recognized that multiple research paradigms are needed to address the characteristics inherently associated with disability research. The importance of applying the highest level of science to work in this field was underscored in a recent special issue of Exceptional Children focusing on the scientific methods and evidence-based practices that guide special education research (Odom et al., 2005). The purpose of this article is to supplement the framework for quality indicators for research in special education presented by Gersten et al. (2005) and to reflect on research on special education technology as a special case. Acknowledging the complexity of this task, the authors begin by offering some caveats, defining several key terms, and reviewing the framework. Subsequently, technology-based examples to the framework indicators are provided and additional indicators are offered. The conclusion presents recommendations for how these quality indicators may be used to enhance the evidence base for the field. It is necessary to offer a few caveats before entering the discussion. First, the discussion and background that accompanies the Gersten et al. (2005) framework has not been reprinted in its entirety. Readers are encouraged to consult the original article for further explanations of the quality indicators. Second, it is not the purpose of this article to provide an introduction to research. For that, readers are encouraged to consult other appropriate sources, such as textbooks (Gall & Borg, 2002; Shadish, Cook, & Campbell, 2002) and Web sites such as HyperStat Online (http://davidmlane.com/hyperstat/), Research Methods Knowledge Base (http://www.socialresearchmethods.net/kb/), and the technical working papers developed by the What Works Clearinghouse (2006).


Journal of Special Education Technology Third, there exists the potential to confuse the work presented in this article with the long-standing efforts by the Quality Indicators of Assistive Technology (QIAT) Consortium (QIAT Consortium, 2001). While the current QIAT indicators do not address research, issues raised in this paper have the potential to be incorporated into future versions of the QIAT framework (http://www.qiat.org). For clarification, the authors offer a review of key terms. Blackhurst (2005) has noted that technology in special education can take six forms. He defines the technology of teaching as “instructional approaches that are systematically designed and applied in very precise ways” (p. 8). He observes that instructional technology is a tool for the delivery of instruction (e.g., videotape, multimedia). Assistive technologies are devices and services that provide access, enhance function, increase independence, and improve quality of life for individuals with disabilities. Medical technologies serve critical functions in life support and enhancement and include items such as ventilators, colostomy bags, cochlear implants, and bionic extremities. Technology also can take the form of technology productivity tools such as computer hardware and software systems and specialized devices that enhance personal and professional productivity such as word processors, databases, and fax machines. Finally, he points to information technology that offers access to information and resources such as the Education Resources Information Center (ERIC) system and World Wide Web. The authors concur with Blackhurst’s broad conceptualization of special education technology and propose adding two forms of technology commonly found in the literature. Distance education technologies involve the synchronous or asynchronous delivery of instruction to mediate distance. Universal design for learning (UDL) utilizes technology to anticipate academic diversity by proactively providing technology-based supports for learning, so that all students can access learning opportunities (Rose & Meyer, 2002). The preceding list of eight diverse forms of technology found in special education highlights the need for researchers to clearly define the form of technology they are studying, clearly state the intended purpose of the technology (e.g., Is the software program Inspiration being used as an instructional technology application for all students or assistive technology for one struggling student?), and thoroughly describe the details of a specific


implementation of technology-based intervention. The authors believe quality indicators will be useful to guide research associated with each form of technology used in special education. The field of special education technology has advanced considerably on the basis of innovative product development. While the assistive technology industry has matured in recent years by establishing a trade association (i.e., the Assistive Technology Industry Association [ATIA]) and gaining access to federally funded technology transfer mechanisms—e.g., the National Center for Technology Innovation (NCTI) and the Research Engineering Research Center on Technology Transfer (T2RERC)—insufficient information is available about the pre-market research and development (R&D) processes (Boone & Higgins, 2005; Golden, 2002) used to create most products within the special education technology marketplace. A recent report sheds new light on the challenges that product developers encounter as they seek to address questions about the evidence base for their products (National Center for Technology Innovation, 2005). Product developers have reported a range of concerns in response to requests for additional research and efficacy data about new products. These responses range from denial (e.g., We don’t need any data, or We know it works because we sell lots of them) to genuine concern about how to ethically conduct valid research (e.g., the potential for bias when a company sponsors pre-release research) as well as methodological issues (e.g., How large of a sample size will we need? and How long must the treatment last?). These concerns underscore the significant need for tools such as the proposed quality indicators to assist product developers in bringing new products to market that have a documented evidence base of effectiveness. In advancing a set of quality indicators, the authors are not advocating a particular research methodology or design. A variety of high-quality methodologies are appropriate for assessing an array of research questions. Instead, the focus is on quality indicators for group designs, or for experimental and quasi-experimental research, based on the Gersten et al. (2005) framework. Readers are referred to the following resources for additional information about quality indicators associated with other types of research investigations, including single-subject research designs (Horner et al., 2005), qualitative research designs

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Journal of Special Education Technology (Brantlinger, Jimenez, Klingner, Pugach, & Richardson, 2005), and correlational research (Thompson, Diamond, McWilliam, Snyder, & Snyder, 2005).

Introduction to the Quality Indicators This section provides an interpretation of the indicators from Gersten et al. (2005) as they apply to technology work, as well as supplemental indicators related directly to technology research and development. Again, readers are referred to the original article for the rationale behind the original indicators, focusing here on the implications for technology research.

1.0 Conceptualization of the Research Study Quality Indicator 1.1: A compelling case for the importance of the research is made if an innovative approach is proposed, is based on sound conceptualization, and is rooted in sound research. A cogent argument needs to be made for not only studying the effectiveness of a particular technology intervention or product, but also for how the study will advance the understanding of the relationship between technology and student learning. In the case of product developers, information must be provided about how the proposed product will fulfill unmet needs of the target user. Outcomes such as literacy, employment, and minority representation, for example, have been long-standing issues of concern to special educators and to society in general. Explaining how the study can provide relevant information on these types of issues is a critical part of the process. Quality Indicator 1.2: The conceptualization of the research study is based on the findings of rigorously designed studies that reflect the current knowledge base. Proposed innovative approaches are based on sound conceptualization and solid research. In the context of product development research, if there is no specific relevant previous research, the researcher should clearly describe the literature review process that was used in the study. In order to establish solid conceptual foundations for the research, it is necessary to present related research that establishes the efficacy of the general approach. For example, if a developer creates a new intervention for learning so-

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cial studies content and the literature review reveals no research studies on this specific intervention, the conceptualization of the research should be based on core learning principles. More specifically, this might include research about the relationship between time on task and learning, the importance of vocabulary development for conceptual understanding, or similar research that demonstrates a grounding of the intervention in a broader context of research on effective instruction. Quality Indicator 1.3: The instructional design of the intervention is clearly described. Technology is frequently viewed as a black box intervention. Some contend that special magic is built into the black box resulting in spectacular outcomes that will help anyone who uses it. Unfortunately, the overhyped claims associated with the rollout of each new technology product are seldom realized in practice. Therefore, particular attention must be paid to the instructional design of the technology intervention. Research reviews of the effectiveness of technology invariably conclude that the design of the product is a key determinant in its success or failure to improve outcomes. What unique design attributes are implemented to support students with disabilities that are not found in ordinary learning environments or materials? Given that not everyone has an affinity for technology, what types of users may not realize the expected gains or benefits? What tangible outcomes can reasonably be expected from the product (e.g., reduction in time spent on the task or increased accuracy)? Greater attention to the instructional design of technologies and the anticipated outcomes will strengthen the foundations of science in our field (Boone & Higgins, 2005) and allow advances beyond the abstract notion that technology by itself can improve achievement. The purpose of this quality indicator, therefore, is to explicitly describe the performance support(s) in the intervention by describing the intended outcome(s). Quality Indicator 1.4: The research design is appropriate for the type of evidence sought (i.e., explanatory, single case, comparative, program evaluation, etc.). As noted earlier, there are many types of technology in special education and many purposes associated with research. As a result,


Journal of Special Education Technology the research design for a proposed study should be congruent with its purpose.

ment of a quality indicator in the area of full disclosure is meant to clarify any potential conflicts of interests.

For example, if the research focuses on a product in development, descriptive, case, and program evaluation research designs are appropriate to yield preliminary evidence on the product’s efficacy. Assistive technology studies often use single-subject designs given the individualized nature of the devices and services. Studies assessing the claim of a technology intervention to serve a universal design for learning function must involve experimental and quasi-experimental group designs to illustrate the differential or uniform impact on students with disabilities and their nondisabled peers.

Quality Indicator 2.1: The researcher discloses any financial ties or relationships with vendors of technology products used in the study or institutions involved in data collection. The developer as researcher is often a necessary research model in the proprietary process of bringing new products to market. Similarly, a developer may support after-market research by providing complimentary copies of software. Finally, a teacher conducting action research in her classroom will often use a participant-observer model of research. In each of these cases, the quality of the research has the potential to be compromised because of financial ties, personal relationships, and/or minimal degrees of independence involved.

Quality Indicator 1.5: Valid arguments supporting the proposed intervention, as well as the nature of the comparison group, are presented. Researchers usually do a good job of describing the nature of the intervention condition. Unfortunately, descriptions of the comparison conditions often receive short shrift. Increasingly, methodologists note that it is important to describe the nature of the instruction or support that participants in the comparison group receive. In particular, the comparison group should spend an equivalent amount of time devoted to the same instructional goal, and the dependent measure should be reasonably well aligned with both conditions. There are occasions when a researcher is broaching a new topic and simply wants to know if it leads to change. These would be the only instances where a no-treatment comparison group would be appropriate. Quality Indicator 1.6: The research questions are derived from the purpose of the study and are stated clearly. This indicator applies equally to research conducted with or without technology interventions; see full description in Gersten et al. (2005).

2.0 Full Disclosure The issue of disclosure has been problematic for the field of medical research, but is also relevant to other fields that heavily focus on development, such as special education technology. There are inherent problems for developers. They need evidence that a product works to bring it to market; however, such evidence is considered tainted or self-serving if they conduct the research or sponsor a third party to conduct the study on their behalf. The advance-


The goal of this quality indicator is not to prohibit developers and teachers from conducting their own research. Rather, the purpose is to highlight the need for independent research activities. When it is not possible to be completely independent, any financial or other potentially controversial relationships must be publicly declared in a disclosure statement that is included with the research proposal and any subsequent technical reports or publications. To the extent possible, product developers are encouraged to retain the services of independent third-party evaluators to conduct rigorous research evaluations of pre-market products with the stipulation that no prior review of publication oversight/control will be exerted by the developer.

3.0 Sample Selection A critical issue associated with the generalizability of a research study rests on the procedures for selecting the sample. Too often, samples of convenience to the researchers are overused in education and constrain the application of the research findings to other populations. The purpose of the three quality indicators in this section is to highlight the necessary function that random selection and random assignment play in high-quality research. Quality Indicator 3.1 Sample selection procedures are appropriate for extrapolating the findings to the population. Educational research has been subjected to considerable criticism for relying on intact classrooms and other forms of convenience samples. As a result, researchers

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Journal of Special Education Technology must be very clear about how a sample was selected to participate. Particular effort must be devoted to providing demographic and performance indicators about the sample to illustrate how the sample is similar to the population of interest. In addition, researchers must ensure that the unit of intervention corresponds to the unit of analysis. This has been a major flaw in educational research over the past 30 years. (See the What Works Clearinghouse tutorial Mismatch Between Unit of Assignment and Unit of Analysis, http://www.whatworks. ed.gov/reviewprocess/workpapers.html.) For example, if a product developer seeks to study the value of a new writing intervention for students who struggle with composition tasks, it is necessary to do more than simply locate three students with learning disabilities in a local school. It is important to establish performance criteria, such as identifying all fifth-grade students whose state writing score is in the bottom quartile and have written expression goals on their IEP, as the basis for creating a pool of potential students, and then randomly selecting a number of students to participate in the research. Quality Indicator 3.2: A power analysis is provided to describe the adequacy of the minimum cell size. The analysis must take into account clustering at the classroom, school, community college, and/or activity center setting. Earlier power estimates typically did not include these factors. When data have been reanalyzed using appropriate corrections for clustering, many formerly significant effects are no longer significant. The What Works Clearinghouse (http://www.whatworks.ed.gov) includes many such examples. Quality Indicator 3.3: Characteristics of the sample reflect the characteristics of the population. Recent research examining the characteristics of the subjects in published research in special education has revealed significant discrepancies in the representation of subjects in research studies versus their representation in the population (e.g., gender [Porter, Christian, & Poling, 2003] and race [Lindo, 2006]). Researchers should indicate how their sample reflects the characteristics of the population on relevant variables such as disability, race, gender, socioeconomic status, age, urban/rural, and so on. Data such as those provided by the United States Census Bureau or the National Assess-

8

ment of Education Progress can provide a demographic profile against which researchers can compare their particular sample. In some cases it may be appropriate to oversample a particular population. For example, a technology researcher interested in the technology skills and attitudes of novice teachers (one to five years post-Baccalaureate degree) versus veteran teachers (20 years post-Baccalaureate degree) may have to oversample veteran teachers (who are less prevalent in the population of special education teachers due to attrition) to make valid comparisons.

4.0 Description of Participants In addition to providing essential information about how the sample was selected, researchers must provide an adequate description of study participants. Quality Indicator 4.1: Sufficient information is presented to determine/confirm whether the participants demonstrated the disability(ies) or difficulties addressed. Researchers must provide an operational definition of the relevant disability(ies) or difficulties and include information from a recently administered assessment to provide a clear sense of the nature of the sample. Other important information includes co-morbidity disability status (e.g., the percentage of students with reading disabilities who also had a math disability and/or attention deficit hyperactivity disorder) and demographics (e.g., age, race, sex, subsidized lunch status, English language learner status, and special education status). For example, statements about participants’ lack of previous use of technology should be supported by performance data using a common task (e.g., failure to activate a single-switch-operated radio). Quality Indicator 4.2: Appropriate procedures are used to increase the probability that participants are comparable across conditions. Whenever possible, random assignment of participants to conditions is recommended. At times, this has been problematic, but a sea change is happening in schools, with increased understanding of why randomized controlled trials are critical if educational research is to begin to display the rigor of studies in public health, medicine, and other sciences. Quality Indicator 4.3: Differential attrition among intervention groups or severe overall attrition is documented.


Journal of Special Education Technology When severe overall attrition can be anticipated, researchers should initially oversample. If natural attrition is large, researchers are encouraged to (a) determine whether or not there is significant differential attrition between groups and (b) use a relevant pretest variable to assess whether or not the final sample is significantly higher or lower performing than the participants who left the study. At the present time, little is known about attrition rates in special education technology research; therefore, researchers and editors are encouraged to publish attrition rates. Quality Indicator 4.4: Sufficient information with characteristics of the intervention providers is described and appropriate procedures to increase the probability that intervention providers were comparable across conditions are used. Providing specific information about intervention providers and procedures is critical; see full description in Gersten et al. (2005).

5.0 Implementation of the Intervention Quality Indicator 5.1: The technology components (e.g., hardware, software, Web sites, and distance education) of the intervention are clearly described. When technology is implemented as part of an intervention, complete descriptions of the hardware, software, Web sites, and so on must be provided. In the case of hardware, researchers should report not only the product name but the model number. In cases where hardware is specially created, researchers should include a photo of the device that illustrates its basic shape and size. Regarding software, complete bibliographic information should be provided including the name, developer, version number, and operating system. When Web sites are utilized, researchers must provide the URL and the date(s) it was accessed. It is suggested that researchers consider using the Web archive function within their Web browser to save an archival copy of the Web site to their computer. They may also want to consider making screen prints to illustrate the Web site or to demonstrate how the Web site functioned. Similarly, distance education research should provide a complete description of the hardware and software tools that were used, as noted above, and make periodic archives that illustrate snapshots in the development and delivery of instruction. Instructional labels that are often assigned to interventions are vague or misleading and may vary considerably


from study to study. For example, while assistive technology is the correct label for interventions using Kurzweil 3000, Read and Write Gold, and WYNN, a more exact title for a study on the reading components of these technologies would be scan-and-read systems. Quality Indicator 5.2: The length of the treatment is appropriate. Decisions associated with the length of treatment are critical to the quality of a study. For example, consider the task of trying to teach someone to use a new switch. In the single-subject tradition, there are good decision rules on how to interpret the performance data relative to maintaining or changing the intervention. However, if one is trying to assess the impact of a writing technology, how long should the intervention continue? We recommend that, at a minimum, the treatment lasts for a quarter of the school year (nine weeks). However, we typically value studies of longer-term interventions (i.e., those that last a semester or an entire school year). Quality Indicator 5.3: Fidelity of implementation is described and assessed in terms of surface (the expected intervention is implemented) and quality (how well the intervention is implemented) features. This issue is discussed extensively in Gersten, Baker, and Lloyd (2000) and in Woodward (1993) for technology. To describe the nature of experiences in experimental and comparison groups, researchers are moving increasingly beyond surface level fidelity checklists to the use of sophisticated observational systems, often leading to a richer interpretation of findings. Quality Indicator 5.4: The nature of services and materials provided in comparison conditions are fully described and documented. One of the least glamorous and most neglected aspects of research is describing and assessing the nature of the instruction in the comparison group. Yet, to understand what an obtained effect means, one must understand what happened in the comparison classrooms. Therefore, researchers should also describe, assess, and document implementation in comparison groups. At a minimum, researchers should examine comparison groups to determine what instructional events are occurring, what texts are being used, and what professional development and support is provided to teachers. Other factors to assess include possible access to the curriculum/content associated with the experimental group’s intervention, time allocated for instruction, and type of

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Journal of Special Education Technology grouping used during instruction. In some studies, assessment of the comparison condition may be similar to what occurs for treatment fidelity. Every study will vary based on specific research issues. Quality Indicator 5.5: Efforts to rule out the impact of concurrent interventions are described. One problem intrinsic to assistive technology outcomes research involves isolating the impact of concurrent interventions (Smith, 2000, 1996). That is, is the outcome the result of the assistive technology device, the assistive technology support services, or occupational therapy that was also provided during the study? Thus, researchers must be cognizant of the impact of concurrent interventions that are occurring while the study is being conducted. In studies involving assistive technology or medical technologies, researchers should provide evidence that they have contacted significant stakeholders (e.g., teachers, parents, case managers) to inquire about concurrent interventions during the time of the study. Likewise, in studies involving instructional technology, universal design, or distance education, researchers should provide evidence that they have conducted a scan of the learning environment to discover any concurrent instructional events that may impact their study. For example, a study on spelling checkers discovers a new spelling curriculum is being implemented in the students’ language arts classroom or that one student recently got a new computer at home and is now using a predictive word processor that facilitates correct spelling. The purpose of this quality indicator is to rule out the impact of concurrent interventions as an alternative explanation for the results of the study. In the event that concurrent interventions are discovered, they should be disclosed in the limitations section of the research report. Ideally, researchers will deploy systemic measures to assess the presence, frequency, and intensity of concurrent interventions.

6.0 Outcome Measures Quality Indicator 6.1: Multiple measures are used to provide an appropriate balance between measures closely aligned with the intervention and the measures of generalized performance. The technical adequacy of the outcome measures used in a research study directly influences the quality of the study and the confidence that can be placed in its results. In special education research that includes a wide range of different types of students, it is critical that

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measures are able to adequately assess the performance of students with different disabilities. This may involve the use of accommodations or of alternate formats such as text-to-speech for content-area reading passages, or structured interviews on the content rather than written essays (Browder, Fallin, Davis, & Karvonen, 2003; Johnson, Kimball, Brown, & Anderson, 2001; Shriner & Destefano, 2003; Ysseldyke et al., 2001). Quality Indicator 6.2: Evidence of reliability and validity for the outcome measure is provided. One of the major issues impacting the quality of assistive technology research is the lack of quality outcome measurement instruments (RESNA, 1998a, 1988b, 1998c). As a result, special education technology researchers must pay particular attention to identifying outcome measures with appropriate technical adequacy. Quality Indicator 6.3: Outcomes for capturing the intervention’s effects are measured at the appropriate times. Little is presently known about the pattern of data snapshots that is required to provide an accurate picture of the outcomes of assistive technology (Edyburn, FennemaJansen, Harihan, & Smith, 2005). As a result, researchers should carefully consider a schedule for data collection that will reveal the various phases involved in acquiring new knowledge, behavior, or attitudes associated with technology use. Quality Indicator 6.4: Data collectors and/or scorers are blind to study conditions and equally (un)familiar to examinees across study conditions. Problems with models in which the researcher is the data collector—as is often the case with unfunded research, master’s and doctoral research, and vendor development research—inherently impacts the quality of a research study. Whenever possible, it is essential to use data collectors and scorers who are independent and blind to the purposes, conditions, and participants in a study. Increasingly, the field of special education and the broader educational community encourage the use of independent external evaluators to conduct research on the effectiveness of a technology product or software product. This is a major shift in educational research and development. We encourage developers to conduct initial evaluation research, perhaps involving small quasi-experiments to see if there are any impacts. The major role of evaluation, however, should shift to independent researchers.


Journal of Special Education Technology Quality Indicator 6.5: Adequate inter-scorer agreement is documented. Researchers should ensure that data collection and scoring is consistent and reliable. See Gersten et al. (2005) for a full description.

7.0 Data Analysis Quality Indicator 7.1: Data analyses and research questions are aligned with the appropriate unit of analysis for each research question. Readers are referred to the Gersten et al. (2005) article and the What Works Clearinghouse (http://www.whatworks.ed.gov/) for information on this topic. There is little that is unique to data analysis in special education technology projects. However, researchers in this area can easily become overwhelmed by data overload since fine-grained performance measures are often easily obtainable. We suggest working with a measurement expert to determine which data to analyze and deciding, in advance, how these data will be scaled and utilized. Quality Indicator 7.2: The chosen data analysis techniques are appropriate and linked in an integral fashion to key research questions and hypotheses. The unit of analysis should be fully linked to the key statistical analyses; see Gersten et al. (2005) for a full description of this indicator. Quality Indicator 7.3: The variability within each sample is accounted for either by sampling or statistical techniques such as analysis of covariance. Post-intervention procedures are used to adjust for differences among groups of more than 0.25 of a standard deviation on salient pretest measures. In addition, planned (a priori) comparisons are used when appropriate.

8.0 Publication and Dissemination The final quality indicator focuses on issues of publication and dissemination of the research findings. Given the prolonged timeline associated with traditional publication processes (e.g., one to two years), it is essential that researchers ethically carry out their responsibility to publish and disseminate the results of their work. In addition, researchers are encouraged to design strategies that bridge the research-to-practice gap into the knowledge products they disseminate.


Quality Indicator 8.1: The researcher demonstrates a clear commitment to publish the research results in a timely manner using appropriate outlets to reach consumers, practitioners, researchers, and the product development community. Many observers have commented on the notion of “Internet time” that challenges developers to alter traditional development cycles from months to days. As a result, most products in the marketplace have a very short shelf life before they are replaced with a newer model featuring either incremental improvements or major upgrades. The pace of time is much faster in the marketplace than in traditional journal publishing contexts. Thus, it has become increasingly common to see published research reports describing a product that is no longer available in the marketplace. While researchers are encouraged to use traditional journal outlets for publishing the results of their research, they must also be attentive to the realities of Internet time and the need to explore the full palette of publishing and disseminating outlets at their disposal. Some viable options for publication and dissemination that reduce the wait time include research briefs, technical reports, and white papers that can be made available on project Web sites, refereed electronic journals, conference presentations and proceedings, and online events. Researchers should provide evidence in their funding proposal of a commitment to use a rich array of dissemination outlets (e.g., Listservs, online publications, conferences, and professional organizations) designed to reach targeted groups of consumers (e.g., parents, teachers, and administrators), researchers, and the product development community. To the extent possible, researchers should also provide evidence of their previous record in publishing the results of their research. Quality Indicator 8.2: The researcher provides appropriate strategies to bridge the research-to-practice gap. Recent interest in knowledge utilization focuses on the ways in which research is used to improve professional practice. Toward that end, researchers must provide evidence that they are acting on their responsibility to facilitate the transfer of research-based knowledge into practice (Malouf & Schiller, 1995; Warby, Greene, Higgins, & Lovitt, 1999). Research implementation efforts by Lovitt (1991) illustrate a promising practice.

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Recommendations for the Application of the Special Education Technology Quality Indicators Currently, there is a great deal of deliberation and discussion on what it means to call an educational practice or special education intervention “evidence-based.” Particularly controversial issues include the relative weightings of randomized trials versus quasi-experiments and to what extent we can generalize the findings across various subgroups of students with disabilities. Another key issue is how to make the determination that an evidence-based practice is implemented with such low quality that it is unlikely to enhance learner outcomes. The special education technology quality indicators outlined in this article are summarized in Table 1, along with three variations of each indicator (i.e., unacceptable, essential, and desirable). We believe these rubrics will serve to initiate conversation within the research community about what the quality indicators look like in practice and help shape a shared vision about quality special education technology research. It is important to note that the challenge of conducting quality research on technology is more expensive than conducting experiments that are compromised at various levels. There are signs of awareness of this fact in both the National Research Council’s (1999) report on educational research and in some current federal initiatives. Routine conduct of quality research in public schools will require a shift in the culture of schools, much as it required a shift in the culture of medical clinics and hospitals 50 years ago, and of public welfare programs two decades ago. Active support by the U. S. Department of Education will mostly likely be required, as it goes hand in hand with the current emphasis on scientifically based research. This version of the special education technology quality indicators should be viewed merely as the initial step in engaging the profession in a dialogue focused on enhancing the quality of our research knowledge base. This document will likely be significantly revised as the diverse needs of the profession are considered. Additionally, the authors foresee study of the similarities and differences with parallel efforts by the American Education Research Association (2006) and the International Society for Technology in Education (Bull, Knezek, Roblyer, 12

Schrum, & Thompson, 2005) to define and measure quality research. Ultimately, this system must undergo field test validation as professionals review grant applications and other research proposals and manuscripts submitted to journals for publication.

References American Education Research Association. (2006). Standards for reporting on empirical social science research. Retrieved on August 15, 2006, from http://www.aera.net/?id=1480 Blackhurst, A. E. (2005). Historical perspectives about technology applications for people with disabilities. In D. Edyburn, K. Higgins, & R. Boone (Eds.), Handbook of special education technology research and practice (pp. 3-29). Whitefish Bay, WI: Knowledge by Design. Boone, R., & Higgins, K. (2005). Designing digital materials for students with disabilities. In D. Boruch, R. F. (Ed.), (1997). Randomized experiments for planning and evaluation: A practical guide. Thousand Oaks, CA: Sage Publications. Brantlinger, E., Jimenez, R., Klingner, J., Pugach, M., & Richardson, V. (2005). Qualitative studies in special education. Exceptional Children, 71(2), 195-207. Browder, D. M., Fallin, K., Davis, S., & Karvonen, M. (2003). Consideration of what may influence student outcomes on alternative assessment. Education and Training in Developmental Disabilities, 38(3), 255-270. Bull, G., Knezek, G., Roblyer, M. D., Schrum, L., & Thompson, A. (2005). A proactive approach to a research agenda for educational technology. Journal of Research on Technology in Education, 37(3), 217-220. DeRuyter, F. (1997). The importance of outcome measures for assistive technology service delivery systems. Technology and Disability, 6, 89-104. Edyburn, D. L. (2004). 2003 in review: A synthesis of the special education technology literature. Journal of Special Education Technology, 19(4), 57-80. Edyburn, D. L. (2005). Assistive technology and students with mild disabilities: From consideration to outcome measurement. In D. Edyburn, K. Higgins, & R. Boone (Eds.), Handbook of special education technology research and practice (pp. 239-269). Whitefish Bay, WI: Knowledge by Design. Edyburn, D. L., Fennema-Jansen, S., Harihan, P., & Smith, R. (2005). Assistive technology outcomes: Implementation strategies for collecting data in schools. Assistive Technology Benefits and Outcomes (e-journal). Available at: http://www.atia.org/atob/ATOBWeb/ATOBV2N1/Documents/EdyburnATOBV2N1.pdf Edyburn, D., Higgins, K., & Boone, R. (Eds.) (2005). Handbook of special education technology research and practice. Whitefish Bay, WI: Knowledge by Design.


Journal of Special Education Technology Fein, J. (1996). A history of legislative support for assistive technology. Journal of Special Education Technology, 13(1), 1-3. Gall, J. P., & Borg, W. R. (2002). Educational research: An introduction. White Plains, NY: Pearson Allyn & Bacon. Gersten, R., Baker, S., & Lloyd, J. W. (2000). Designing high quality research in special education: Group experimental design. Journal of Special Education, 34, 2-18. Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. (2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional Children, 71(2), 149-164.

Odom, S. L., Brantlinger, E., Gersten, R., Horner, R. H., Thompson, B., & Harris, K. R. (2005). Research in special education scientific methods and evidence-based practices. Exceptional Children, 71(2), 137-148 Okolo, C. M., Bahr, C. M., & Rieth, H. J. (1988). A retrospective review of computer-based instruction. Journal of Special Education, 12(1), 1-27. Office of Technology Assessment. (1982). Technology and handicapped people. Washington, DC: U. S. Government Printing Office. Porter, C. L., Christian, L., & Poling, A. (2003). Research brief: Some data concerning the reporting of participants’ gender in the mental retardation literature. Mental Retardation, 41(2), 75-77.

Golden, D. C. (2002). Instructional software accessibility: A status report (Guest Columnist). Journal of Special Education Technology, 17(1), 57-60.

QIAT Consortium. (2001). Quality indicators for assistive technology services. Special Education Technology Practice, 3(1), 14-15.

Hannaford, A. E. (1993). Computers and exceptional individuals. In J. D. Lindsey (Ed.), Computers and exceptional individuals (pp. 3-26). Austin, TX: Pro-Ed.

Rehabilitation Engineering and Assistive Technology of North America (RESNA). (1998). Volume I: RESNA resource guide for assistive technology outcomes: Measurement tools. Arlington, VA: Author.

Hauser, J., & Malouf, D. B. (1996). A federal perspective on special education technology. Journal of Learning Disabilities, 29(5), 504511.

Rehabilitation Engineering and Assistive Technology of North America (RESNA). (1998). Volume II: RESNA resource guide for assistive technology outcomes: Assessment instruments, tools, & checklists from the field. Arlington, VA: Author.

Horner, R. H., Carr, E. G., Halle, J., McGee, G., Odom, S., & Wolery, M. (2005). The use of single-subject research to identify evidence-based practice in special education. Exceptional Children, 71(2), 165-180. HyperStat Online. (n.d.). Retrieved December 13, 2006, from http:// davidmlane.com/hyperstat/ The Individuals with Disabilities Education Act Amendments of 1997, Public Law 105-17, 105th Congress, 1997. Johnson, E., Kimball, K., Brown, S. O., & Anderson, D. (2001). A statewide review of the use of accommodations in large-scale, high-stakes assessments. Exceptional Children, 67, 251-264. Lindo, E. (2006). The African American presence in reading intervention experiments. Remedial and Special Education, 27(3), 148153. Lovitt, T. C. (1991). Preventing school dropouts: Tactics for at-risk, remedial and mildly handicapped adolescents. Austin, TX: Pro-Ed. Malouf, D. B., & Schiller, E. P. (1995). Practice and research in special education. Exceptional Children, 61(5), 414-424. Mosteller, F., & Boruch, R. (Eds.). (2002). Evidence matters: Randomized trials in education research. Washington, DC: The Brookings Institution. National Center for Technology Innovation. (2005). Moving toward solutions: Assistive and learning technology for all students. Washington: DC: American Institutes for Research. National Research Council. (1999). How people learn: brain, mind, experience, and school. J. Bransford, A. Brown, & R. Cocking (Eds.), Committee on Developments in the Science of Learning, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.


Rehabilitation Engineering and Assistive Technology of North America (RESNA). (1998). Volume III: RESNA resource guide for assistive technology outcomes: Developing domains of need and criteria of services. Arlington, VA: Author. Research Methods Knowledge Base. (n.d.). Retrieved December 13, 2006, from http://www.socialresearchmethods.net/kb/ Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age. Alexandria, VA: Association for Supervision and Curriculum Development. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for general causal inference. Boston, MA: Houghton Mifflin. Shavelson, R. J., & Towne, L. (Eds.). (2002). Scientific research in education. Washington, DC: National Academy Press. Shriner, J. G., & Destefano, L. (2003). Participation and accommodations in state assessment: The role of individualized education programs. Exceptional Children, 69(2), 147-161. Smith, R. O. (Ed.). (1996). Measuring assistive technology outcomes: Theoretical and practical considerations. Assistive Technology, 8(2), 71-130. Smith, R. O. (2000). Measuring assistive technology outcomes in education. Diagnostique, 25(4), 273-290. The Technology-Related Assistance for Individuals with Disabilities Act of 1988, Public Law 100-407, 100th Congress, 1988. Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Snyder, S. W. (2005). Evaluating the quality of evidence from correlation research for evidence-based practice. Exceptional Children, 71(2), 181-194.

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Journal of Special Education Technology Warby, D. B., Greene, M. T., Higgins, K., & Lovitt, T. C. (1999). Suggestions for translating research into classroom practice. Intervention in School and Clinic, 34, 205-211, 223.

Woodward, J., & Rieth, H. (1997). A historical review of technology research in special education. Review of Educational Research, 67(4), 503-536.

What Works Clearinghouse Technical Working Papers. (n.d.). Retrieved December 13, 2006, from http://www.whatworks.ed.gov/ reviewprocess/workpapers.html

Ysseldyke, J., Thurlow, M., Bielinski, J., House, A., Moody, M., & Haigh, J. (2001). The relationship between instructional and assessment accommodations in an inclusive state accountability system. Journal of Learning Disabilities, 34, 212-220.

Whitehurst, G. J. (2003). The Institute of Education Sciences: New wine, new bottles. Paper presented at the annual conference of the American Educational Research Association, Los Angeles. Retrieved January 17, 2005, from http://www.ed.gov/rschstat/ research/pubs/ies.html Willis, J. (2003). Instructional technologies in schools: Are we there yet? Computers in the Schools, 20(1/2), 11-33. Woodward, J. (1993). The technology of technology-based instruction: Comments on the research, development, and dissemination approach to innovation. Education and Treatment of Children, 16, 345-360.

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Author Notes Russell Gersten is Executive Director of the Instructional Research Group in Long Beach, CA, and Professor Emeritus at the University of Oregon. Dave L. Edyburn is a Professor in the Department of Exceptional Education, University of Wisconsin-Milwaukee. Correspondence should be addressed to Dave Edyburn, Department of Exceptional Education, University of Wisconsin-Milwaukee, P.O. Box 414, Milwaukee, WI 53201. Email to edyburn@ uwm.edu



Table 1 Rubrics for Assessing Special Education Technology Quality Indicators Area

1.0. Conceptualization of the Research Study

Quality Indicator

Unacceptable

Essential

1.1: A compelling case for the importance of the research is made.

No evidence of previous research using this technology has been identified.

1.2: The conceptualization of the research study is based on the findings of rigorously designed studies that reflect the current knowledge base. Proposed innovative approaches are based on sound conceptualization and solid research.

Fails to provide informa- Describes how multiple tion on how the review of sources and strategies literature was conducted. were used to conduct the literature review. Research proposal or report fails to demonstrate Research proposal or rehow the work builds port clearly delineates how and extends current the work builds and exknowledge. tends current knowledge. Citations of popular literature are used to justify the need for the study.

1.3: The instructional design of the intervention is clearly described.

Technology will help everyone who uses it.

1.4: The research design is appropriate for the type of evidence sought (i.e., explanatory, single case, comparative, program evaluation, etc.). 1.5: Valid arguments supporting the proposed intervention, as well as the nature of the comparison group, are presented.

The research design is not appropriate for the described purposes.

1.6: The research questions are derived from the purpose of the study and are stated clearly.

Potential impact and value of this new technology for individuals with disabilities is clearly demonstrated.

Citations demonstrate current knowledge of the seminal and recent research literature on the topic. Provides evidence of principles specifically applied to enhance the functional performance of individuals with disabilities.

Desirable

Illustrates how the research will add to the knowledge base and how it demonstrates the impact and value of the technology. Evidence of a comprehensive review of the literature and critical analysis of what is known. Research proposal or report critically analyzes the strengths and weakness of the best research evidence available.

Citations illustrate knowledge of seminal, recently published, and in-progress works. Clearly targets the intended beneficiary and profiles the expected gains and identifies those individuals/ groups that are not expected to benefit. The research design is The research design is apappropriate for the type of propriate and evidence is evidence sought. provided on why/how this methodology will extend previous research.

Fails to connect the current work with the research literature.

The intervention is grounded in the research literature.

Fails to attend to issues associated with control groups or multiple baselines. The research questions are presented without adequate grounding in the knowledge base.

The research design provides for a control group or multiple baselines.

The intervention is clearly defined and contrasted with other interventions of known impact.

Clearly defined procedures are outlined for the control group. The research questions are The research questions a logical extension of what are logical, focused, and is known and not known. measurable.

Note. The information in this table is adapted from Tables 1 & 2, from “Quality indicators for group experimental and quasi experimental research in special education,” by R. Gersten, L. S. Fuchs, D. Compton, M. Coyne, C. Greenwood, and M. S. Innocenti, Exceptional Children, 71(2), 2005, 149-164. Copyright 2005 by The Council for Exceptional Children. Adapted with permission.


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Table 1 (continued) Rubrics for Assessing Special Education Technology Quality Indicators Area

Quality Indicator

2.0. Full Disclosure

2.1: The researcher discloses any financial ties or relationships with vendors of technology products used in the study or institutions involved in data collection.

3.0. Sample Selection

4.0. Description of Participants

Essential

Desirable

The researcher fails to address the issue of disclosure.

The researcher discusses any financial ties or relationships with the journal editor or funding agency to clarify any perception of impropriety.

No information is provided on attrition.

Attrition for each group is Attrition for each group is documented. documented and evaluated to assess its impact.

Inadequate information is provided concerning the intervention providers to assure that they were comparable across conditions.

Detailed descriptions demonstrate that intervention providers were comparable across conditions.

The researcher submits a signed statement describing any financial ties or relationships with vendors of technology products used in the study or institutions involved in data collection. 3.1 Sample selection pro- Convenience sample Students are randomly Students are randomly cedures are appropriate for of students selected for assigned to treatment and selected and randomly extrapolating the treatment and study lacks control conditions. assigned to treatment and findings to the population. control group. control conditions. 3.2: A power analysis is No information is proEvidence that a power Evidence that a power provided to describe the vided on how the sample analysis was conducted analysis was conducted for adequacy of the minimum size was determined. for the primary variables each analysis that will be cell size. A power analyis provided as a rationale conducted is provided as a sis is conducted for each for determining adequate rationale for determining analysis to be examined. sample size. adequate sample size. 3.3: Characteristics of the No information is proEvidence is provided that Detailed evidence is sample reflect the characvided on how the sample the sample is reflective of provided on how the teristics of the population. reflects the population. the characteristics of the statistical properties of population on at least one the sample reflect the important variable. population. 4.1: Sufficient information The researcher only reThe researcher demonDetailed evidence is is presented to determine/ ports that the participants strates how he or she provided that quantitaconfirm whether the parqualified for disability reaffirmed the disability tive measures were used ticipants demonstrated the services using an existing characteristics and quali- to assess the presence of a disability(ies) or difficulties programmatic evaluation fications to participate in disability and/or perforaddressed. system. the study. mance problem that correlates with the diagnostic level. 4.2: Appropriate proceNo procedures are deRandom assignment is Random selection and dures are used to increase scribed for assessing the used to assign participants random assignment are the probability that parequivalency of participants to groups. used to assign participants ticipants are comparable across groups. to groups. across conditions. 4.3: Differential attrition among intervention groups or severe overall attrition is documented. 4.4: Sufficient information with characteristics of the intervention providers is described and appropriate procedures to increase the probability that intervention providers were comparable across conditions are used.

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Unacceptable

Detailed descriptions demonstrate that intervention providers were comparable across conditions and fidelity measures were used to assess the quality of the implementation.




Quality Indicator

5.0. Implementation 5.1: The technology comof the Intervention ponents (e.g., hardware,

software, Web sites, and distance education) of the intervention are clearly described.

5.2: The length of the treatment is appropriate.

5.3: Fidelity of implementation is described and assessed in terms of surface (the expected intervention is implemented) and quality (how well the intervention is implemented) features. 5.4: The nature of services and materials provided in comparison conditions are fully described and documented.

6.0. Outcome Measures

Unacceptable

Essential

The technology components used in the intervention are mentioned vaguely.

Detailed information is provided concerning each of the technology components used in the intervention. If the product is custom-made, a photo of the device [or screen print of the software/Web site] is included. A rationale for the length of the treatment is provided.

Product numbers, model numbers, and version numbers are provided concerning each technology component used in the intervention.

Surface and quality fidelity of implementation is assessed using a single observer over time.

Surface and quality fidelity of implementation is assessed using multiple independent observers over time.

The length of the treatment is inadequate and unlikely to produce measurable change in performance. Fidelity of implementation is not assessed.

Insufficient information is provided about the technology products and services that were provided.

Detailed information is provided about the technology products and services provided to the intervention and control groups to permit replication of the research.

Inadequate information is available on the reliability and validity of the outcome measures.

Reliability and validity measures for each assessment instrument are appropriate (.6 for new measures, .8 for established measures).

Desirable

The length of treatment is a minimum of one semester and ideally one school year.

Detailed information is provided about instructional attributes or power of the technology products and services provided to the intervention and control groups to permit replication of the research. 5.5: Efforts to rule out Insufficient information is Detailed information is Efforts are made to meathe impact of concurrent provided about concurrent provided about efforts sure the provision of all interventions are described. interventions that may taken to isolate the impact concurrent interventions impact the results of the of concurrent intervenin order to understand research. tions that may impact the their impact on the results results of the research. of the research. 6.1: Multiple measures Outcome measures focus Multiple measures are Multiple measures are are used to provide an ap- exclusively on near-trans- used to assess performance used to measure perforpropriate balance between fer tasks to the exclusion in near-transfer tasks as mance in (a) near-transfer measures closely aligned of measures of generalized well as far-transfer tasks. tasks (b) transfer tasks, (c) with the intervention and performance. with technology, and (d) the measures of generalized without technology. performance. 6.2: Evidence of reliability and validity for the outcome measure is provided.


The outcome measurement instruments reflect the highest technical adequacy available for measuring the constructs.

17



6.0. Outcome Measures (continued)

Quality Indicator

Essential

Desirable

The data collection schedule is based on reasonable assumptions associated with the learning curves of the technologies being used and the acquisition patterns of the intended learning/performance outcomes. 6.4: Data collectors and/or Inadequate information Independent data colscorers are blind to study is provided to rule out lectors and/or scorers conditions and equally researcher bias relative to are used to guard against (un)familiar to examinees data collection and scoring researcher bias. across study conditions. procedures. 6.3: Outcomes for capturing intervention effects are measured at the appropriate times.

The schedule for collecting data is illogical or inadequate for measuring changes in the dependent measures.

Fidelity scores reveal a minimum of .90 agreement. 7.1: Data analyses and The research questions are Analysis procedures are research questions are not aligned with the apappropriate. aligned with the appropri- propriate unit of analysis ate unit of analysis for each or appropriate analysis research question. procedures. 7.2: The chosen data Analysis procedures are Analysis procedures are analysis techniques are not appropriate for the appropriate. appropriate and linked in type of data or are not an integral fashion to key designed to answer the research questions and research questions. 6.5: Adequate inter-scorer agreement is documented.

7.0. Data Analysis

Unacceptable

hypotheses. 7.3: The variability within each sample is accounted for either by sampling or statistical techniques such as analysis of covariance.

Fidelity scores are inadequate or missing.

No evidence is provided that the researcher has accounted for variability within each sample.

Post-intervention procedures are used to adjust for differences among groups of more than 0.25 of a standard deviation on salient pretest measures.

8.0. Publication and 8.1: The researcher demon- The researcher fails to strates a clear commitment demonstrate a comDissemination to publish the research mitment to publish results in a timely manner the research results, or using appropriate outlets publication outlets are not to reach consumers, prac- appropriate to reach the titioners, researchers, and designated stakeholders.

The researcher demonstrates a commitment and previous track record for timely publication of research results in appropriate outlets to reach the designated stakeholders.

The researcher fails to provide evidence of strategies to move the research into practice.

The researcher outlines appropriate strategies for bridging the research-topractice gap.

the product development community. 8.2: The researcher provides appropriate strategies to bridge the research-topractice gap.

18

The schedule for data collection is informed by previous research and based on commonly recognized intervals associated with learning curves.

Detailed descriptions are provided to clearly indicate how independent data collectors and/or scorers are used to guard against researcher bias. Fidelity scores exceed .95 agreement. The research questions are aligned with the appropriate unit of analysis, and appropriate analysis procedures are outlined. Analysis procedures are appropriate for answering the research questions.

The researcher examines variability within samples by sampling or using appropriate statistical techniques and describes the implications of the results. The researcher presents a publication plan for disseminating the results of the research to designated stakeholders using multiple publication outlets.

Practitioners are involved in key junctures of the research to design strategies that will facilitate the use of the research in the improvement of professional practice.



Research About Assistive Technology: 2000-2006. What Have We Learned? Cynthia M. Okolo Michigan State University Emily C. Bouck Purdue University The purpose of this article is to offer a review of research on the use of assistive technology for students with disabilities and some reflections on the nature of knowledge that is being produced by researchers who are examining these issues. We analyzed studies published in peer-reviewed journals between 2000 and 2006 that investigated the use of assistive technology with students served under IDEA guidelines. We located 122 studies that met our criteria. We summarize the types and age of students with whom these studies were conducted, the outcomes, the types of designs used, and the journals in which the studies were published. We conclude with a discussion of implications for research and practice.

E

ducational research has been subjected to intense scrutiny over the past five years, culminating in our current focus on scientific research in education (Feuer, Towne, & Shavelson, 2002; Shavelson & Towne, 2002). Special education research has been subjected to these increased expectations, and scholars have crafted standards by which we can judge the quality of research that includes students with disabilities (Brantlinger, Jiminez, Klingner, Pugach, Richardson, 2005; Gersten et al., 2005; Horner, Carr, Haley, & McGee, 2005; Odom et al., 2005; Thompson, Diamond, McWilliam, Snyder, & Snyder, 2005). In this issue, Gersten and Edyburn offer comprehensive standards to guide our evaluation of the quality of research about the use of technology in special education. Judgments about the quality of research should go hand in hand with a basic understanding of the nature and scope of the research. Research reviews can serve this purpose. By summarizing information across studies, they enable us to draw conclusions about the issues, technologies, and populations about whom we have the most information. In addition, they can illuminate gaps


in the extant knowledge base. Technology-based applications develop at a rapid pace, and their implications for the education of students with disabilities are constantly evolving. While keeping abreast of research-based knowledge, therefore, is a challenge (Edyburn, 2000), it is an important endeavor for informing best practices. Over the past decade, special educators have published a few comprehensive reviews of assistive technology that span a broad range of students with disabilities. However, several of these reviews were conducted more than 10 years ago (e.g., Fitzgerald, 1996; Okolo, Bahr, & Rieth, 1993; Woodward & Rieth, 1993). More recently, Alper and Raharinirina (2006) reviewed 68 studies conducted between 1988 and 2003. About half of them focused on students diagnosed with learning disabilities (LD). Eighty percent were designed to improve the skills of participants. The authors concluded that students with visual impairments were neglected in this body of research, and that future research should include more attention to the relationship between types of assistive technology (AT) and their application to different types of disabilities.

19

Journal of Special Education Technology Edyburn (2000, 2003, 2004) has conducted annual “comprehensive one-year reviews” of special education technology research. His reviews have examined both research-based and practitioner-oriented publications and offer insight into issues that are of most interest to the field. Viewed over time, they show both continuities and changes in the attention given to different populations of students and AT. Gersten and Edyburn (this volume) discuss some of the implications of these reviews. Another source that has compiled information about AT research is the Handbook of Special Education Technology Research and Practice (Edyburn, Higgins, & Boone, 2005). Many of its chapters include comprehensive reviews of AT use and outcomes for students with different disabilities, as well as across different content areas (e.g., mathematics, social studies, literacy). However, few recent reviews have undertaken a review of AT research that synthesizes studies across disability categories and types of technology-based applications. This article addresses the need for such a review by examining the complete body of AT research from 2000 to 2006 as undertaken with students served under IDEA guidelines. In doing so, we hope to contribute an additional perspective to our understanding of the research base for AT use in our schools. In this review, we analyze only those studies that collected empirical data. Although much of the information in the professional literature focuses on issues of practice (see Gersten & Edyburn, this volume), our focus is on the research base, as disseminated in peer-reviewed publications. We acknowledge that this focus represents only one way to represent the state of knowledge in our field, and that much important and informative work is available in publications that discuss product development; explain technology applications; and delineate curricula, models of AT use, and professional development practices. However, our efforts are focused on summarizing the nature of empirical work in the field in an effort to (a) capture the types of research being conducted, (b) highlight strengths and gaps in our knowledge base, and (c) pose questions that can guide future research. Two aspects of this article warrant a brief explanation. First, we will use the term assistive technology (AT) throughout the paper. However, as explained below, we chose to review only studies that investigated interactive technologies; thus, we excluded work focused on appli-

20

cations that also would fit under the federal definition of AT, including low-technology solutions and devices designed to compensate for functional limitations. Second, we did not attempt to evaluate the quality of the research we reviewed. Rather, our emphasis in this paper is on the nature of that research, the topics it addresses, and the students with whom it was conducted.

Method Selection of Studies We began by searching the electronic databases WilsonSelectPlus and ProQuest, using all possible combinations of these descriptors: technology, special education, disability, and impairment. We examined the results of these searches for papers that met the following criteria. The study was designated by the electronic database as published in a peer-reviewed journal between 2000 and 2006. Studies published prior to 2000 were excluded, and studies published in 2007 were not available to us at the time this paper was written. The authors studied the use of AT by individuals with disabilities, with the intent of better understanding or improving academic, social, communication, or behavioral outcomes. In addition, we examined studies that investigated ways to help teacher educators and service providers either learn more about AT or use AT more effectively with students. We did not include studies in which technology was used only as a medium to deliver content to teachers, such as studies in which teachers took part in professional development about literacy instruction in a Web-based course or studies in which preservice teachers learned about a particular disability through a multimedia case study. Although these types of studies used technologies to affect an outcome for students with disabilities, their goals were not those of helping teachers or other service providers use AT in educational settings. The technology used in the study involved some degree of interactivity. Thus, we did not include studies that used technology in static ways, as was the case in studies of prompting systems, video used only to present instructional content, video feedback, and video modeling. Nor did we include articles that focused primarily on devices used to improve specific physical, sensory, or medical JSET Volume 22, Number 3

Journal of Special Education Technology conditions (e.g., cochlear implants). A few studies we examined focused on issues of interface design (e.g., children’s preferences for displays and switches). However, the majority of human-factors research located was conducted with adults, and therefore not included. The study included students served under IDEA 2004 guidelines. We restricted our analyses to studies that included students, aged birth to 21, who were diagnosed with a disability. We did include studies that involved students who were considered at risk for learning or behavioral disabilities. We excluded studies that examined only the use of AT with postsecondary students or adults. However, we included studies that included postsecondary learners and/or adults if any students from birth to 21 also were included in the sample. The author(s) asked a research question and collected data, in some form, to answer that question. As discussed above, we did not include articles that offered only reports of product development, advice to educators or others about how to use AT, models of AT use or implementation, and presentations of professional development practices. Although reviews of research did not meet this criterion, we examined their reference lists to determine if we had missed any potential articles in our electronic searches. Furthermore, we examined reference lists from relevant chapters in the Handbook of Special Education Technology Research and Practice (Edyburn, Higgins, and Boone, 2005) to locate any studies that might have been overlooked in our search. Adhering to these criteria, we located 122 studies for analysis, ranging from 12 studies in 2004 to 22 studies in 2002. Despite our efforts to conduct a thorough search, we recognize that, inadvertently, we may have overlooked some studies that met our criteria. However, we are confident that our search procedures were sufficient to capture the majority of the research articles published between 2000 to 2006.

Analysis of the Studies We read the abstract of each study identified in our searches to determine if it met the above criteria. For those studies that did, we examined the article further to obtain the following information.


Target group. This category represents the group of individuals with disabilities for whom the technology applications investigated in the study were intended. For each study, we coded one primary disability group (e.g., learning disabilities, mild disabilities). In some cases, the target group was different from the study participants. For example, students without disabilities were the participants in some studies that sought to examine their attitudes toward or opinions of students who used augmentative and alternative communication (AAC). Hence, we coded the target group for these studies speech/language disability. Furthermore, although teacher education students and service providers were often the sole or primary participants in a study, we coded the intended beneficiaries of the intervention or technology applications provided to the adult participants. Some authors studied specific diagnostic categories, others used cross-categorical designations (e.g., mild disabilities, severe disabilities). Using the authors’ descriptors of their participants, we recorded, for each study, one or more of 13 different target groups: (a) disabilities in general, (b) LD or reading disabilities, (c) cognitive disabilities, (d) speech/language disabilities, (e) physical disabilities, (f ) behavior disabilities, (g) severe disabilities/ multiple/developmental disabilities, (h) mild disabilities, (i) visual impairment, (j) ADHD, (k) autism, (l) hearing impairment, and (m) English language learners with disabilities. Age range. Here, we noted the age of participants in each study. Unlike our coding for the target group (above), we considered the age range of the study participants themselves. Because many studies spanned more than one age range (e.g., preschool and elementary school participants, or middle school through high school participants), we coded multiple age ranges for each study. We used the following ranges in our analysis: (a) early childhood (ages 0-4), (b) elementary school (ages 5-10), (c) middle school (ages 11-14), (d) high school (ages 1521), (e) post-high school, (f ) teacher education students, and (g) service providers. The category teacher education students was used to designate participants in a preservice preparation program, and service providers was used to designate study participants who were inservice educators, administrators, related service providers, and family members.

21

Journal of Special Education Technology Type of outcome. In this category, we examined the outcomes of primary interest in the study, using broad category labels to capture a variety of related outcomes under the same designation. Several studies focused on several broad outcomes, and we assigned these to multiple categories. Outcomes categories are: (a) communication, (b) employment, (c) functional/self-help skills, (d) implementation, (f ) literacy skills, (g) other academics (not literacy), (i) social/emotional, (j) technology knowledge, and (k) other. Several of these categories warrant additional comment. Technology knowledge refers to the acquisition of more positive attitudes or improved knowledge or skills about assistive technology use among teacher educators or service providers. Implementation refers to studies that examined the ways in which technology was being used in educational or home settings. Several studies that examined the effectiveness of or students’ preferences for specific technology features (e.g., scanning, switches) were coded as other. Methods. As might be expected, the methods used in the studies were quite diverse. Rather than attempting to assign studies to tightly defined categories, we used a broad categorization scheme that required fewer inferences. Thus, we coded the primary method used in a study as either: (a) descriptive, (b) intra-individual, or (c) comparison. Descriptive studies aimed to describe individual, group, or organizational (e.g., classroom, school, district, program) use of AT and, often, the factors that influenced that use. These studies typically used survey or case study methods. Intra-individual studies collected data that enabled the researcher(s) to compare an individual’s performance over time and/or across different conditions. These studies used single-subject designs. Group comparison studies focused on examining differences among groups of students over time or among conditions. Methods included correlational, quasi-experimental, and experimental designs. This category scheme does not imply that certain types of studies were more valuable than others; we believe that all three types of methods provide important information to the field (e.g., Odom et al., 2005). Nor did we attempt to evaluate the quality of the research conducted in these studies.

22

Results Target Group As shown in Table 1, 17% (n = 21) of the studies we reviewed did not focus on any particular disability group, but rather, explored issues of relevance to students with disabilities in general. Studies that investigated issues related to teacher preparation or the use of technology in a particular school or program are examples of research targeting disabilities in general. Studies that examined a range of students with mild disabilities accounted for 7% (n=9) of this group of studies. Studies designating their target group as students with mild disabilities were typically those investigating the implementation of a technology-based intervention in an inclusive classroom. Among the studies that focused on a specific disability group, speech and language disability was the most frequent, investigated in 29% (n=35) of the studies in this analysis. The next most frequent target group was learning or reading disabilities, accounting for about 18% (n= 22) of the studies. Between 5% and 10% of the studies focused on severe/multiple/developmental disabilities (n=10) and cognitive disabilities (n=6). No other specific disability group accounted for more than 5% of the studies we reviewed, although the reader should note that any target group might have been included in the general disabilities category.

Age Range As shown in Table 2, about two thirds of participants in the studies were in elementary, middle, or high school, with an approximately equal distribution across these age ranges. Over 1 in 10 studies (11%) included children with disabilities in early childhood (ages 0 to 5). Teacher education students and service providers were represented in more than one third (36%) of the studies.

Outcomes As shown in Table 3, 39% of the studies addressed the use of technology to improve students’ academic skills (i.e., literacy, 32%, other academics, 7%). Studies focusing on the implementation of technology were also prev-



Table 1 Target Group of Students with Disabilities Target Group

2000

2001

2002

2003

2004

2005

2006

Speech/language disabilities

6

3

9

4

3

5

5

35 (29%)

LD or reading disabilities

4

4

3

2

1

3

5

22 (18%)

1

9

1

2

1

5

2

21 (17%)

2

1

2

1

0

1

3

10 (8%)

Mild disabilities

0

2

3

2

2

0

0

9 (7%)

Cognitive disabilities

0

0

1

1

3

1

0

6 (5%)

Behavior disabilities

1

0

0

2

0

1

1

5 (4%)

Visual impairment

0

0

3

0

0

0

1

4 (3%)

Physical disabilities

0

0

0

0

2

0

1

3 (2%)

Autism

1

0

0

0

0

1

1

3 (2%)

Hearing impairment

0

0

0

0

0

2

0

2 (2%)

ADHD

1

1

0

0

0

0

0

2 (2%)

English language learners

0

1

0

0

0

0

0

1 (1%)

General disabilities (disabilities in general) Severe/multiple/developmental disabilities

Total

Table 2 Participants’ Age Age Range

2000

2001

2002

2003

2004

2005

2006

Total

Early childhood

6

2

1

2

1

1

4

17 (11%)

Elementary

6

9

6

1

3

4

2

31 (20%)

Middle school

4

7

8

2

5

4

4

34 (22%)

High school

5

3

4

4

7

6

6

35 (23%)

Teacher education students

0

3

0

3

0

1

0

7 (5%)

Service providers

1

5

8

2

1

7

5

29 (19%)

Note. An individual study might include participants from more than one age group. Thus, the denominator for these percentages is 153.


23


Methods

alent in this sample, accounting for 23% of the studies reviewed. Student outcomes other than academic skills included social/emotional outcomes (10%), communication skills (9%), and functional/self-help skills (7%), such as purchasing and organizational skills. Also present were studies that focused on preparing professionals to use technology with students who have disabilities (i.e., technology knowledge, 8%). Not surprisingly, given our focus on students served by IDEA, only 2% of the studies focused on outcomes related to employment.

As demonstrated in Table 4, group comparison was the most frequently used method, accounting for 40% of the studies we reviewed. The number of group comparison studies nearly doubled between 2005 and 2006, perhaps because of the federal government’s emphasis on research that employs experimental designs. Studies that produced descriptive data, primarily through surveys and case studies, accounted for 39% of the research reviewed. Intra-individual comparisons, primarily through singlesubject designs, were used in 21% of the studies.

Table 3 Type of Outcome Outcome

2000

2001

2002

2003

2004

2005

2006

Total

Literacy

5

5

9

4

6

6

6

41 (32%)

Implementation

2

7

6

2

2

6

4

29 (23%)

Functional/self-help

1

4

1

1

2

0

0

9 (7%)

Social/emotional

1

2

3

2

1

3

1

13 (10%)

Communication

6

0

0

3

0

2

0

11 (9%)

Technology knowledge

0

2

2

2

0

3

1

10 (8%)

Academics (other than literacy)

0

1

1

1

2

1

3

9 (7%)

Other

0

0

0

0

2

0

2

4 (3%)

Employment

1

0

0

0

0

1

0

2 (2%)

Note. An individual study might include participants from more than one age group. Thus, the denominator for these percentages is 128.

Table 4 Study Methods Study Method

2000

2001

2002

2003

2004

2005

2006

Comparison

7

8

5

6

6

6

11

49 (40%)

Description

5

8

10

6

3

9

6

47 (39%)

Intra-individual

4

5

7

2

3

4

1

26 (21%)

24

Total



Literature Scatter Edyburn (2000) contended that one way to better understand the evolution of research about special education technology is to examine the range of journals in which the research is published. Table 5 provides information about the journals from which the studies we analyzed were drawn. The 122 studies examined for this review were published in 27 different journals. Two journals accounted for about two thirds of the studies: 40% were published in the Journal of Special Education Technology and 21% were published in Augmentative and Alternative Communication. The number of publications per journal dropped sharply after that, with no other journals accounting for more than five studies across the course of this six-year review span. Most papers were published in special education journals. The range of special education journals in which the articles were located is quite extensive, however, spanning journals deigned to appeal to a broad audience (e.g., Exceptional Children), disability-specific journals (e.g., Journal of Visual Impairment and Blindness, Learning Disability Quarterly), and teacher education journals (e.g., Teacher Education and Special Education). Only a handful of studies were published in technology journals (e.g., Learning, Media, and Technology, Journal of Research on Computing in Education) or journals aimed at more general education audiences (e.g., Reading Research and Instruction).

Discussion and Conclusions In this review, we identified 122 articles that met our inclusion criteria; that is, research-based publications about AT for students served under IDEA. We located almost twice the number of research-based articles than did Alper and Raharinirina (2006), who found 68 papers published between 1998 and 2003. Perhaps this discrepancy indicates that the frequency of AT research is on the rise. However, we located substantially fewer articles than Edyburn has in his annual reviews. For example, Edyburn (2000) located 114 articles published in 1999 and 224 articles published in 2003 (Edyburn, 2004). Edyburn synthesized articles that were “judged to be relevant if [they] expressly mentioned technology and individuals with disabilities in the contexts associated with schools and learning” (Edyburn, 2003, p. 7). A


comparison of the number of articles we identified over a six-year span to the number of articles found by Edyburn on an annual basis supports the contention that the majority of information about AT addresses “issues of practice” (Gersten & Edyburn, this volume). Over a decade ago, Okolo and colleagues (Okolo, Cavalier, Ferretti, & MacArthur, 1995) characterized AT research as “scattershot” and lacking much evidence of programmatic foci. With some notable exceptions, the same observation can be applied to the body of work reviewed here. Lines of programmatic research were most evident in studies of AT for students with speech and language disabilities. Nearly one third of the studies reviewed focused on these students, and most of these studies were published in the journal Augmentative and Alternative Communication. The prevalence of students with speech and language disabilities in our sample is partially explained by the degree to which this population uses AAC. However, the body of research conducted with this group of students is notable for its systematic nature. Clear and accumulative lines of research, such as attitudes toward students who use AAC and the organization and displays of AAC devices, were evident among these studies. In addition, we noted a defined line of research in which students with cognitive disabilities were taught purchasing skills through technology-based applications. However, although many studies addressed the same broad content (e.g., literacy), we found only a few studies about any particular topic. Furthermore, the disabilities and ages of participants varied from study to study, making it difficult to draw conclusions about the efficacy of any particular application or approach. As might be expected, students with high-incidence disabilities (that is, LD, behavior disorders, attention deficit-hyperactivity disorders, and the noncategorical designation mild disabilities) accounted for nearly onethird of the participants in these studies. It was encouraging to find that many of these studies focused on the implementation or evaluation of AT in general education classrooms. This suggests that, at least for these students, AT is being investigated in the real-life contexts in which it is most likely to be used. However, students with physical and sensory impairments, who are likely to derive extensive benefits from AT, were rarely the focus of these studies. Similarly, Alper and Raharinirina (2006) noted

25


Table 5 Location of Publications Journal

Publication Year and Number of Studies Analyzed 2000

2001

2002

2003

2004

2005

2006

Total


5

7

8

3

6

11

9

49

Augmentative and Alternative Communication

5

1

6

3

3

4

3

25

British Journal of Educational Technology

1

3

0

1

0

0

0

5

Education and Training in Mental Retardation and Developmental Disabilities

1

2

0

1

0

0

0

4

Journal of Visual Impairment and Blindness

0

1

3

0

0

0

0

4

Remedial and Special Education

0

1

0

1

0

0

1

3

Journal of Speech, Language, and Hearing Science

0

0

0

1

0

1

1

3

Topics in Early Childhood Special Education

0

0

0

0

0

0

2

2

Learning Disability Quarterly

0

0

2

0

0

0

0

2

Journal of Research on Technology in Education

0

1

0

1

0

0

0

2

Journal of Rehabilitation Research

0

1

0

1

0

0

0

2

Teaching Exceptional Children

0

0

2

0

0

0

0

2

Learning Disability Quarterly

0

0

2

0

0

0

0

2

Journal of Technology and Teacher Education

0

1

0

0

0

0

0

1

Action in Teacher Education

0

0

0

1

0

0

0

1

Teacher Education & Special Education

0

0

0

1

0

0

0

1

Exceptionality

0

0

0

1

0

0

0

1

Exceptional Children

0

0

0

0

1

0

0

1

Learning Disabilities: A Multidisciplinary Journal

0

1

0

0

0

0

0

1

Technology and Childhood Education Annual

0

1

0

0

0

0

0

1

Reading Research and Instruction

0

1

0

0

0

0

0

1

Focus on Autism & Other Developmental Disabilities

0

0

0

0

0

1

0

1

Learning Disabilities Research and Practice

0

0

1

0

0

0

0

1

Behavior Disorders

1

0

0

0

0

0

0

1

Journal of Early Intervention

1

0

0

0

0

0

0

1

Learning, Media, & Technology

0

0

0

0

0

1

0

1

Journal of Research on Computing in Education

1

0

0

0

0

0

0

1

26


Journal of Special Education Technology that students with visual impairment were underrepresented in the studies they reviewed. We would concur, but add that students with other sensory and physical impairments also represent a very narrow section of the research base. When considering the ages of students in these studies, it seems clear that researchers have done a good job investigating issues of relevance across the K-12 range. Furthermore, students from birth to age 4 were included in over 10% of the studies, which suggests that AT is playing an important role in educational issues for very young children. The prevalence of teacher education students and service providers (that is, teachers, related service personnel, and families) also is promising. We have ample documentation that lack of teacher education, professional development, and family involvement are major barriers to more effective use of technology (e.g., Lahm, 2005). Our analysis indicates that researchers are paying attention to these issues. Literacy skills remain a primary concern of AT researchers, as about one third (32%) of the studies targeted students’ literacy skills. Studies of AT implementation also were common among those we reviewed, accounting for 23% of the sample. Implementation studies were primarily descriptive, however, and in only a few cases evaluated the impact of a particular model or set of practices. It was interesting to note that social/emotional issues, which most often entailed investigations of attitudes and social interactions, were the focus of 10% of the studies. This finding suggests that researchers are not only concerned with functional outcomes such as skill development, but also with the social and psychological well-being of the students who use these technologies. The primary research methods used in the studies were designs that compared groups of students across time or conditions (40%) and designs that offered qualitative information about the use and impact of AT (39%). Single-subject designs also were frequent, evident in 21% of the studies. Such designs have a long history in the field of special education (e.g., Thompson et al, 2005), and seemed well suited in these studies to the questions they addressed. Although experimental designs are widely considered the best design for documenting the effectiveness of an intervention (Gersten et al., 2005), such designs were rare


in this set of studies. We noticed a trend toward more well-controlled studies in 2004 through 2006, with an increasing number of quasi- and experimental designs in more recent studies. Quality standards developed by Gersten and Edyburn (this issue) and others are critical for the field as we attempt to expand the quantity and quality of our research base. Absent from this body of research were some of the designs that instructional technology researchers have found most useful in investigating the features and impact of technology-based interventions, such as design experiments (Brown, 1992) and designed-based research (Sandoval & Bell, 2004). Similarly, AT researchers’ knowledge of single-subject design, a methodology almost absent in the general educational technology literature, could make considerable contributions to more general research about technology’s use and impact in educational settings. Finally, when considering literature scatter, we observed a narrower range of publication outlets than Edyburn has documented in his yearly reviews. Although the studies we analyzed were spread across 27 journals, 60% were published in just two: Journal of Special Education Technology and Augmentative and Alternative Communication. This finding might seem reassuring—keeping abreast of AT research may not require extensive searches of the literature or subscriptions to multiple journals. However, the lack of publications in more general special education and education journals raises the concern that we are “preaching to the choir.” Thus, it seems incumbent upon AT researchers to make a stronger effort to inform those educators whose professional interests may not be so closely aligned with AT that this body of research is relevant to their practices on behalf of students with disabilities. As discussed above, the conclusions one can draw from this review are limited by its scope—interactive technologies for K-12 students with disabilities. Undoubtedly, there is a wealth of other information about AT that is available to special educators, general educators, service providers, teacher educators, and families. Literature about product development and product descriptions, models for AT use, and recommendations for effective practice serves a very important function in informing best practices and in guiding future research. However, based on this review, one might question the

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Journal of Special Education Technology strength of the research base on which such information is founded.

*Alobiedat, A. (2005). Comparing pre-service technology standards with technology skills of special educators in Southwestern Michigan. International Journal of Instructional Media, 32, 385-395.

To say “we need more research” is to repeat an overused admonition, but we believe such a conclusion is warranted. Our analysis leads us to many of the same conclusions offered by Silver-Pacuilla (2006), which are based on nine forums conducted with AT experts in 2004 and 2005. Among other issues, participants in these forums stressed the need for building capacity in AT research through better communication, multidisciplinary collaboration in the design and conduct of research, and improved teacher education and professional development.

Alper, S., & Raharinirina, S. (2006). Assistive technology for individuals with disabilities: A review and synthesis of the literature. Journal of Special Education Technology, 21(2), 47-64.

Finally, a wealth of topics awaits the attention of AT researchers. As discussed above, information about the design and efficacy of AT for low-incidence disabilities is minimal, as is any systematic body of research about the design, fit, or impact of specific types of technologybased applications for improving literacy outcomes for students with mild disabilities. We located only a few studies of speech technology (Zhao, this issue) and no studies of emerging technologies such as gaming (Shaffer, this issue) or portable, mobile technologies (e.g., cell phones). As discussed in Bouck, Okolo, and Courtad (this issue), studies about the relationship between students’ use of technology at school and at home also are nonexistent. No study we reviewed has the phrase “universal design” in its title. Furthermore, despite their importance in helping students make optimal use of technologies, only a few studies examined how technology features, such as the interface itself, affected students. These issues seem particularly important as technology applications continue to migrate to the Web, which may affect the ways in which we will be able to learn and access information in the future. The needs and voices of students with disabilities need clearer representation in all of these developments.

References* *Abbott, C., & Cribb, A. (2001). Special schools, inclusion, and the World Wide Web—the emerging research agenda. British Journal of Educational Technology, 32, 331-342. *Abner, G. H. (2002). Implementation of assistive technology with students who are visually impaired: Teachers’ readiness. Journal of Visual Impairment and Blindness, 96, 98-105.

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*Anderson, C. L., & Petch-Hogan, B. (2001). The impact of technology use in special education field experience on preservice teachers’ perceived technology expertise. Journal of Special Education Technology, 16(3), 27-44. *Angelo, D. H. (2000). Impact of augmentative and alternative communication devices on families. Augmentative and Alternative Communication, 16, 37-47. *Ashton, T. M. (2005). Perceived knowledge, attitudes, and challenges of AT use in special education. Journal of Special Education Technology, 20(2), 60-63. *Ayres, K. M., & Langone, J. (2002). Acquisition and generalization of purchasing skills using a video enhanced computer-based instructional program. Journal of Special Education Technology, 17, 15-28. *Basham, J., Palla, A., & Pianfetti, E. (2005). An integrated framework used to increase preservice teachers NETS-T Ability. Journal of Technology and Teacher Education, 13, 257-276. *Beck, A. R., Bock, S., Thompson, J. R,. & Kosuwan, K. (2002). Influence of communicative competence and augmentative and alternative communication technique on children’s attitudes toward a peer who uses AAC. Augmentative and Alternative Communication, 18, 217-227 *Beck, A. R., Fritz, H., Keller, A., & Dennis, M. (2000). Attitudes of school-aged children who use augmentative and alternative communication. Augmentative and Alternative Communication, 16, 239-249. *Beck, A. R., Kingsbury, K., Neff, A., & Dennis M. (2000). Influence of length of augmented message on children’s attitudes toward peers who use augmentative and alternative communication. Augmentative and Alternative Communication, 16, 239-249. *Beck, A. R., Thompson, J. R., Clay, S. L., Hutchins, M., Vogt, W. P., Romaniak, B., & Sokolowski, B. (2001). Preservice professionals’ attitudes toward children who use augmentative/alternative communication. Education and Training in Mental Retardation and Developmental Disabilities, 36, 255-271. *Beck, J. (2002). Emerging literacy through assistive technology. Teaching Exceptional Children, 35(2), 44-48. *Blankenship, T. L., Ayres, K. M., & Langone, J. (2005). Effects of computer-based cognitive mapping on reading comprehension for students with emotional behavior disorders. Journal of Special Education Technology, 20(2), 15-23. *Boon, R. T., Burke, M. D., & Fore III, C. (2006). The impact of cognitive organizers on student success in secondary social studies classrooms. Journal of Special Education Technology, 21(1), 5-16. *Bottge, B. A., Heinrichs, M., Chan, S., Mehta, Z. D., & Watson, E. (2003). Effects of video-based and applied problems on the procedural math skills of average- and low-achieving adolescents. Journal of Special Education Technology, 18(2), 5-22.


Journal of Special Education Technology Brantlinger, E., Jiminez, R., Klingner, J., Pugach, M., & Richardson V. (2005). Qualitative studies in special education. Exceptional Children, 71, 195-208.

*DiCarlo, C. F., & Banajee, M. (2000). Using voice output devices to increase initiations of young children with disabilities. Journal of Early Intervention, 23, 191-199.

Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2(2), 141-178.

*Dimitriadi, Y. (2001). Evaluating the use of multimedia authoring with dyslexic learners: A case study. British Journal of Educational Technology, 32, 265-275.

*Burgstahler, S., & Cronheim, D. (2001). Supporting peer-peer and mentor-protégé relationships on the Internet. Journal of Research on Technology in Education, 34, 59-74.

*Drager, K. D. R. (2003). The performance of typically developing 2 ½ year olds on dynamic display AAC technologies with different system layouts and language organizations. Journal of Speech, Language, and Hearing Research, 46, 298-312.

*Burke, R., Beukelman, D., R., Ball, L., & Horn, C. A. (2002). Augmentative and alternative communication technology learning part 1: Augmentative and alternative communication specialists. Augmentative and Alternative Communication, 18, 242-249. *Burke, R., Beukelman, D., R., Ball, L., & Horn, C. A. (2002). Augmentative and alternative communication technology learning part 2: Preprofessional students. Augmentative and Alternative Communication, 18, 250-254. *Buzhardt, J., Abbott, M., Greenwood, C., & Tapia, Y. (2005). Usability testing of the class wide peer tutoring-learning management system. Journal of Special Education Technology, 20(1), 19-29. *Carter, M. (2003). Communicative spontaneity of children with high support needs who use augmentative and alternative communication systems I: Classroom spontaneity, mode, and function. Augmentative and Alternative Communication, 19, 141-154. *Carter, M. (2003). Communicative spontaneity of children with high support needs who use augmentative and alternative communication systems II: Antecedents and effectiveness of communication. Augmentative and Alternative Communication, 19, 155-169. *Cohen, W., Hodson, A., O’Hare, A., Boyle, J., Durrani, T., McCartney, E., Mattey, M., Naftalin, L., & Watson, J. (2005). Effects of computer-based intervention through acoustically-modified speech (Fast ForWord) in severe mixed expressive-receptive language impairment. Journal of Speech and Hearing Science, 48, 715729. *Cole, J., & Swinth, Y. (2004). Comparison of the TouchFree Switch to a physical switch: Children’s abilities and preferences: A pilot study. Journal of Special Education Technology, 19(2), 19-30. *Colwell, C., Jeffs, A., & Mallett, E. (2005). Initial requirements of deaf students for video: Lessons learned from an evaluation of a digital video application. Learning, Media, and Technology, 30, 201-207. *Corn, A. (2002). Access to multimedia presentations for students with visual impairments. Journal of Visual Impairment and Blindness, 96, 197-211 *Dattilo, J., Guerin, N., Cory, L., & Williams, R. (2001). Effects of computerized leisure education on self-determination of youth with disabilities. Journal of Special Education Technology, 16(1), 5-17. *Dawson, L., Venn, M. L., & Gunter, P. L. (2000). The effects of teacher versus computer reading models. Behavioral Disorders, 25, 105-113.


*Dudek, K., Beck, A. R., Thompson, J. R. (2006). The influence of AAC device type, dynamic vs. static screen, on peer attitudes. Journal of Special Education Technology, 21, 27-28. *Dugan, L. M., Campbell, P. H., & Wilcox, M., J. (2006). Making decisions about assistive technology with infants and toddlers. Topics in Early Childhood Special Education, 26(1), 25-33. Edyburn, D. L. (2004). 2003 in review: A synthesis of the special education technology literature. Journal of Special Education Technology, 19(4), 57-80. Edyburn, D. L. (2003). 2002 in review: A synthesis of the special education technology literature. Journal of Special Education Technology, 18(3), 5-26. Edyburn, D. L. (2000). 1999 in review: A synthesis of the special education technology literature. Journal of Special Education Technology, 15(1), 7-18 Edyburn, D., Higgins, K., & Boone, R. (Eds.). (2005). Handbook of special education technology research and practice. Whitefish Bay, Wisconsin: Knowledge by Design. *Elliot, L. B., Foster, S., & Stinson, M. (2003). A qualitative study of teachers’ acceptance of speech-to-text transcription system in high school and college classrooms. Journal of Special Education Technology, 18(3), 45-59. *Ely, R., Emerson, R. W., Maggiore, T., Rothbert, M., O’Connell, T., & Hudson, L. (2006). Increased content knowledge of students with visual impairments as a result of extended descriptions. Journal of Special Education Technology, 21, 31-40. *Epstein, J. N., Willis, M. G., Conners, C. K., & Johnson, D. E. (2001). Use of a technological prompting device to aid a student with attention deficit hyperactivity disorder to initiate and complete daily tasks: An exploratory study. Journal of Special Education Technology, 16(1), 19-28. *Faris-Cole, D., & Lewis, R. (2001). Exploring speech recognition technology: Children with learning and emotional/behavioral disorders. Learning Disabilities: A Multidisciplinary Journal. 11(1), 3-12. *Ferretti, R. P., MacArthur, C. D., & Okolo, C. M. (2001). Teaching for historical understanding in inclusive classrooms. Learning Disability Quarterly, 24, 59-71. Feuer, M. J., Towne, L., & Shavelson, R. J. (2002). Scientific culture and educational research. Educational Researcher, 31, 4-14. Fitzgerald, G. E. (1996). Empirical advances in technology-assisted instruction for students with mild and moderate disabilities. Journal of Research on Computing in Education, 28, 526-553.

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Journal of Special Education Technology Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. (2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional Children, 71, 149-165.

Horner, R. H., Carr, E. G., Halle, J., McGee, G., Odom, S., & Wolery, M. (2005). The use of single-subject research to identify evidence-based practice in special education. Exceptional Children, 71, 165-180.

*Goldsworthy, R. C., Barab, S. A., Goldsworthy, E. L. (2000). The STAR project: Enhancing adolescents’ social understanding through video-based, multimedia scenarios. Journal of Special Education Technology, 15(2), 13-26.

*Howell, R. D., Erickson, K., Stanger, C., & Wheaton, J. E. (2000). Evaluation of a computer-based program on the reading performance of first grade students with potential for reading failure. Journal of Special Education Technology, 15(4), 5-14.

*Greenwood, C. R., Arreaga-Mayer, C., Utley, C., Gavin, K. M., & Terry, B. J. (2001). Class wide peer tutoring learning management system: Applications with elementary-level English Language Learners. Remedial and Special Education, 22, 34-47.

*Hutcherson, K., Langone, J., Ayres, K., & Clees, T. (2004). Computer-assisted instruction to teach items selection in grocery stores: An assessment of acquisition and generalization. Journal of Special Education Technology, 19(4), 33-42.

*Gregor, P., Dickinson, A., Macaffer, A., & Andreasen, P. (2003). SeeWord? A personal word processing environment for dyslexic computer users. British Journal of Educational Technology, 34, 321355.

*Hunt, P., Soto, G., Maier, J., Muller, E., & Goetz, L. (2002). Collaborative teaming to support students with alternative communication needs in the general education classroom. Augmentative and Alternative Communication, 18, 20-35.

*Gumpel, T. P., & Nativ-Ari-Am, H. (2001). Evaluation of a technology for teaching complex social skills to young adults with visual and cognitive impairments. Journal of Visual Impairment and Blindness, 95, 95-107.

*Irish, C. (2002). Using peg- and keyword mnemonics and computerassisted instruction to enhance basic multiplication performance in elementary students with learning and cognitive disabilities. Journal of Special Education, 17(4), 29-40.

*Hamm, E. M., Mistrett, S. G., & Ruffino, A. G. (2006). Play outcomes and satisfaction with toys and technology of young children with special needs. Journal of Special Education Technology, 21(1), 29-36.

*Jeffs, T., Behrmann, M., & Bannan-Ritland, B. (2006). Assistive technology and literacy learning: Reflections of parents and children. Journal of Special Education Technology, 21(1), 37-44.

*Hetzroni, O. (2002). Augmentative and alternative communication in Israel: Results from a family survey. Augmentative and Alternative Communication, 18, 255-266.

*Jerome, A., & Barbetta, P. M. (2005). The effects of active student responding during computer-assisted instruction on social studies learning by students with learning disabilities. Journal of Special Education Technology, 20(3), 13-23.

*Hetzroni, O. E., & Belfiore, P. J. (2000). Preschoolers with communication impairments play shrinking Kim: An interactive computer storytelling intervention for teaching Blissymbols. Augmentative and Alternative Communication. 16, 260-269.

*Johnson, J. M., Inglebret, E., Jones, C., & Ray, J. (2006). Perspectives of speech language pathologists regarding success versus abandonment of AAC. Augmentative and Alternative Communication, 22, 85-89.

*Hetzroni, O. E., & Lloyd, L. L. (2000). Shrinking Kim: Effects of active versus passive computer instruction on the learning of element and compound Blissymbols. Augmentative and Alternative Communication, 16, 95-106.

*Judge, S. L. (2001). Computer applications in programs for young children with disabilities: Current status and future directions. Journal of Special Education Technology, 16(1), 29-40.

*Hetzroni, O. E., & Shrieber, B. (2004).Word processing as an assistive technology tool for enhancing academic outcomes of students with writing disabilities in the general education classroom. Journal of Learning Disabilities, 37, 143-154. *Higgins, E. L., & Raskind, M. H. (2005). The compensatory effectiveness of the Quicktionary Reading Pen II on the reading comprehension of students with learning disabilities. Journal of Special Education Technology, 20(1), 31-40. *Higgins, E. L., & Raskind, M. H. (2000). Speaking to read: The effects of continuous vs. discrete speech recognition systems on the reading and spelling of children with learning disabilities. Journal of Special Education Technology, 15(1), 19-30. *Hochstein, D. D., McDaniel, M. A., & Nettleton, S. (2004). Recognition and vocabulary in children and adolescents with cerebral palsy: A comparison of two speech coding schemes. Augmentative and Alternative Communication, 20, 45-62.

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*Kapperman, G. (2002). Survey of the use of assistive technology by Illinois students who are visually impaired. Journal of Visual Impairment and Blindness, 96, 106-108. *Kent-Walsh, J., & Light, J. (2003). General education teachers’ experiences with inclusion of students who use augmentative and alternative communication. Augmentative and Alternative Communication, 19, 104-124. *Kim, A., Vaughn, S., Klingner, J. K., Woodruff, A. L., Reutebuch, C. K., & Kouzekanani, K. (2006). Improving the reading comprehension of middle school students with disabilities through computer-assisted instruction. Remedial and Special Education, 27, 235-248. *Kim-Rupnow, W. S., & Burgstahler, S. (2004). Perceptions of students with disabilities regarding the value of technology-based support activities on postsecondary education and employment. Journal of Special Education Technology, 19(2), 43-56. *Koester, H. H. (2004). Usage, performance, and satisfaction outcomes for experienced users of speech recognition. Journal of Rehabilitation Research, 41, 739-755.


Journal of Special Education Technology *Kolter, A. L., & Tam, C. (2002). Effectiveness of using discrete utterance speech recognition software. Augmentative and Alternative Communication. 18, 137-146. *Koul, R., & Hester, K. (2006). Effects of repeated listening experiences on the recognition of synthetic speech by individuals with severe intellectual disabilities. Journal of Speech and Hearing Research, 49, 47-58. *Laffey, J. M., Espinosa, L., Moore, J., & Lodree, A. (2003). Supporting learning and behavior of at-risk young children: Computers in urban education. Journal of Research on Technology in Education, 35, 423-440. Lahm, E. A. (2005). Improving practice using assistive technology knowledge and skills. In D. Edyburn, K. Higgins, & R. Boone (Eds.), (2005). Handbook of special education technology research and practice (pp. 721-746). Whitefish Bay, WI: Knowledge by Design. *Lahm, E. A. (2002). Factors that influence assistive technology decision making. Journal of Special Education Technology, 17(1), 1526. *Lancaster, P. E., Lancaster, S. C., Schumaker, J. B., & Deshler, D. D. (2006). The efficacy of an interactive hypermedia program for teaching a test-taking strategy to students with high-incidence disabilities. Journal of Special Education Technology, 21(2), 17-30. *Lancaster, P. E., Schumaker, J. B., & Deshler, D. D. (2002). The development and validation of an interactive hypermedia program for teaching a self-advocacy strategy to students with disabilities. Learning Disability Quarterly, 25, 277-302. *Lange, A. A., McPhillips, M., Mulhern, G., & Wylie, J. (2006). Assistive software tools for secondary-level students with literacy difficulties. Journal of Special Education Technology, 21(3), 13-22. *Lebel, T., Olshtain, E., & Weiss, P. L. (2005). Teaching teachers about augmentative and alternative communication: Opportunities and challenges of a Web-based course. Augmentative and Alternative Communication, 21, 264-277. *Lee, Y., & Vail, C. O. (2005). Computer-based reading instruction for young children with disabilities. Journal of Special Education Technology, 20(1), 5-18. *Lewis, A., & Neill, S. (2001). Portable computers for teachers and support services working with pupils with special educational needs: An evaluation of the 1999 United Kingdom Department of Education scheme. British Journal of Educational Technology, 32, 301-315. *Light, J. C., Drager, K., McCarthy, J., Mellott, S., Millar, D., Parrish, C., Parsons, A., Rhaods, S., Ward, M., & Welliver, M. (2004). Performance of typically developing four- and five-year old children with AAC systems using different language organization techniques. Alternative and Augmentative Communication, 20, 63-88. *Lilienfeld, M., & Alant, E. (2005). The social interaction of an adolescent who uses AAC: The evaluation of a peer training program. Augmentative and Alternative Communication, 21, 278-294.


*Lilienfeld, M., Alant, E. (2002). Attitudes of children toward an unfamiliar peer using an AAC device and with and without voice output. Augmentative and Alternative Communication, 18, 137146. *Lindstrand, P. (2001). Parents of children with disabilities evaluate the importance of the computer in child development. Journal of Special Education Technology, 16(2), 43-52. *Lynch, L., Fawcett, A. J., & Nicolson, R. I. Computer-assisted reading intervention in a secondary school: An evaluation study. British Journal of Educational Technology, 31, 333-348. *MacArthur, C. A., & Cavalier, A. R. (2004). Dictation and speech recognition technology as test accommodations. Exceptional Children, 71, 43-59. *McCarthy, J., Light, J., McNaughton, D., Grodzicki, L., Jones, J., Panek, E., & Parkin, E. (2006). Re-designing scanning to reduce learning demands: The performance of typically developing 2-year-olds. Augmentative and Alternative Communication, 22, 269-283. *Maki, H. S., Vauras, M. S., & Vainio, S. (2002). Reflective spelling strategies for elementary school students with severe writing difficulties: A case study. Learning Disabilities Quarterly, 25, 189-191. *Maushak, N. J., Kelley, P., & Blodgett, T. (2001). Preparing teachers for the inclusive classroom: A preliminary study of attitudes and knowledge of assistive technology. Journal of Technology and Teacher Education, 9, 419-431. *Mechling, L. C. (2004). Effects of multimedia, computer-based instruction on grocery shopping fluency. Journal of Special Education Technology, 19(1), 23-34. *Mechling, L. C., & Gast, D. L. (2003). Multi-media computerbased instruction to teach grocery word associations and store locations: A study of generalization. Education and Training in Mental Retardation and Developmental Disabilities, 38, 62-76. *Mechling, L. C., Gast, D. L., & Barthold, S. (2003). Multi-media, computer-based instruction to teach students with moderate intellectual disabilities to use a debit card to make purchases. Exceptionality, 11, 239-254. *Mechling, L. C., Gast, D. L., & Langone, J. (2002). Computerbased video instruction to teach persons with moderate intellectual disabilities to read grocery aisle signs and locate items. The Journal of Special Education, 35, 224-240. *Mechling, L. C., & Langone, J. (2000). The effects of a computerbased instructional program with video anchors on the use of photographs for prompting augmentative communication. Education and Training in Mental Retardation and Developmental disabilities, 35, 90-105. *Meyers, B., & Collier, S. (2003). Creating on-line individual education plans: Preservice teachers learn to make data-based decisions. Action in Teacher Education, 25(2), 23-34. *Michaels, C. A., & McDermott, J. (2003). Assistive technology integration in special education teacher preparation: Program coordinators’ perceptions of current attainment and importance. Journal of Special Education Technology, 18(3), 29-41.

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Journal of Special Education Technology *Millar, D. C., Light, J. D., & McNaughton, D. B., (2004). The effect of direct instruction and writer’s workshop on the early writing skills of children who use augmentative and alternative communication. Augmentative and Alternative Communication, 20, 164-178. *Mirenda, P., Turoldo, K., & McAvoy, C. (2006). The impact of word prediction software on the written output of students with physical disabilities. Journal of Special Education Technology, 21(3), 5-12. *Mirenda, P., Wilk, D., & Carson, P. (2000). A retrospective analysis of technology use patterns of students with autism over a five-year period. Journal of Special Education Technology, 15(3), 5-16. *Mitchell, M. J., & Fox, B. J. (2001). The effects of computer software for developing phonological awareness in low-progress readers. Reading Research and Instruction, 40, 315-332. *Montgomery, D. J., Karlan, G. R., & Coutinho, M. (2001). The effectiveness of word processor spell checker programs to produce target words for misspellings generated by students with learning disabilities. Journal of Special Education Technology, 16(2), 27-41. *Moore, D., Chen, Y., McGrath, P., & Powell, N. J. (2005). Collaborative virtual environment technology for people with autism. Focus on Autism and Other Developmental Disabilities, 20, 231243 *Moore H. W., & Wilcox, M. J. (2006). Characteristics of early intervention practitioners and their confidence in using assistive technology. Topics in Early Childhood Special Education, 26(1), 25-24. *Morgan, R. L., Gerity, B. P., & Ellerd, D. A. (2000). Using video and CD-ROM technology in a job preference inventory for youth with severe disabilities. Journal of Special Education Technology, 15(3), 25-33.

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*Mortsen, S. (2002). Action research on cognitive rescaling. Journal of Special Education Technology, 17(4), 52-28.

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*Sheehy, K. (2005). Morphing images: A potential tool for teaching word recognition to children with severe learning disabilities. British Journal of Educational Technology, 36, 293-301.

Odom, S. L., Brantlinger, E., Gersten, R., Horner, R. H., Thompson, B., & Harris, K. R. (2005). Research in special education: Scientific methods and evidence-based practices. Exceptional Children, 71, 137-148. Okolo, C. M., Bahr, C. M., & Rieth, H. J. (1993). A retrospective view of computer-based instruction. Journal of Special Education Technology, 12(2), 1-27. Okolo, C. M., Cavalier, A. R., Ferretti, R. P., & MacArthur, C. A. (1995, January). Projects funded by the Technology, Media, and Materials Program, 1986-1994. What have we learned? Newark: University of Delaware. *Parette, H. P., Huer, M. B., & Brotherson, M. J., (2001). Related service personnel perceptions of team AAC decision-making across culture. Education and Training in Mental Retardation and Developmental Disabilities, 36, 69-82.

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Silver-Pacuilla, H. (2006, January). Moving toward solutions: Assistive and learning technology for all students. Washington, DC: National Center for Technology Innovation. *Slater, J. M. (2002). A pictorial approach for improving literacy skills in students with disabilities. Journal of Special Education Technology, 17(3), 58-62. *Soto, G., Hartmann, E., & Wilkins, D. P. (2006). Exploring the elements of narrative that emerge in the interactions between an 8-year-old child who uses an AAC device and her teacher. Augmentative and Alternative Communication, 22, 231-241. *Soto, G., Muller, E., Hunt, P., & Goetz, L. (2001). Critical issues in the inclusion of students who use augmentative and alternative communication: An educational team perspective. Augmentative and Alternative Communication, 17, 62-72. *Stock, S. E., Davies, D. K., & Wehmeyer, M. L. (2004). Internetbased multimedia tests and surveys for individuals with intellectual disabilities. Journal of Special Education Technology, 19(4), 43-47.


Journal of Special Education Technology *Stoner, M. L., Easterbrooks, S. R., & Laughton, J. M. (2005). Handwritten and word-processed story retellings by school-aged students who are deaf. Journal of Special Education Technology, 20(3), 35-44. *Strum, J. M., & Rankin-Erickson, J. L. (2002). Effects of computergenerated concept mapping on the expository writing of middle school students with learning disabilities. Learning Disabilities Research and Practice, 17, 124-139. *Sutherland, D. E., Gillon, G. G., & Yoder, D. E. (2005). AAC use and service provision: A survey of New Zealand speech-language therapists. Augmentative and Alternative Communication, 21, 295307. *Tam, C., Reid, D., Naumann, S., & O’Toole, B. (2002). Effects of word prediction list on text entry with children with spina bifida and hydrocephalus. Augmentative and Alternative Communication, 18, 147-162. Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Snyder, B. (2005). Evaluating the quality of evidence from correlational research for evidence-based practice. Exceptional Children, 71, 181-195. *Twyman, T., & Tindal, G. (2006). Using a computer-adapted, conceptually based history text to increase comprehension and problem-solving skills of students with disabilities. Journal of Special Education Technology, 21(2), 5-16. *Wilcox, M. J., Guimond, A., Campbell, P. H., & Moore, H. W. (2006). Provider perspectives on the use of assistive technology for infants and toddlers with disabilities. Topics in Early Childhood Special Education, 26(1) 33-50. *Williams, S. C. (2002). How speech-feedback and word prediction software can help students write. Teaching Exceptional Children, 34(3), 72-78.


Woodward, J., & Rieth, H. J. (1997). A historical review of technology research in special education. Review of Educational Research, 67(4), 503-536. *Xin, J. F., & Rieth, H. J. (2001). Video-assisted vocabulary instruction for elementary school students with learning disabilities. Information Technology in Childhood Education Annual, 1, 87-103. *Zhang, Y. (2000). Technology and the writing skills of students with learning disabilities. Journal of Research on Computing in Education, 32, 467-478. *Zorfass, J., & Rivero, H. K. (2005). Collaboration is key: How a community of practice promotes technology integration. Journal of Special Education Technology, 20(3), 41-67.

Note: Articles included in the analysis are preceded by an asterisk.

*

Author Notes Cynthia M. Okolo is a Professor in the Department of Counseling, Educational Psychology, and Special Education, College of Education, Michigan State University. Emily C. Bouck is an Assistant Professor in the Special Education Program, Department of Educational Studies, College of Education, Purdue University. Correspondence should be addressed to Cindy Okolo, 338 Erickson Hall, College of Education, Michigan State University, East Lansing, MI 48824. Email to [email protected] Summary tables of the 122 studies reviewed in this manuscript can be obtained by contacting the first author.

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Speech Technology and Its Potential for Special Education Yong Zhao Michigan State University Speech technology typically refers to technology that enables machines to receive and accept human oral language as input and respond with human or human-like oral language as output. Speech technology has recently become increasingly mature and available to the general public. At the same time, there has been an increase in the interest in using speech technology to support learning for students with disabilities. This article surveys the current capacities of speech technology, reviews its existing and potential uses in education in general and special education in particular, and recommends research and development actions and strategies for realizing the potential of speech technology for learners with disabilities.

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peech technology can enable machines to receive human language as input and respond with oral language as output. To be able to carry on a conversation or interact naturally with machines using oral language is the ultimate dream that has provided the impetus for the development of speech technology. However, this form of technology is still a long way from enabling people to have natural, unstructured conversations with computers that sound like humans, as envisioned for the future by 2001: A Space Odyssey and Star Trek. Regardless, the capacity to recognize human oral language input and produce human-like spoken language output has improved enough in recent years to warrant serious consideration for educational purposes, especially for students with special needs. This article surveys the current capacities of speech technology, reviews its existing and potential uses in education in general and special education in particular, and recommends research and development actions and strategies for realizing the potential of speech technology for learners with special needs.

What Is Speech Technology? Speech technology typically refers to technology that enables machines to receive and accept human oral language as input and respond with human or human-like JSET Volume 22, Number 3

oral language as output. Thus, there are two categories of speech technology, one for input and the other for output. The speech technology for input is often referred to as speech recognition, voice recognition, or speech-to-text, whereas the technology for output is referred to as speech synthesis or text-to-speech.

Speech Recognition Speech recognition technology allows the user to speak to a computer or a similar device and receive recognized speech as text delivered to other computer applications, such as a word processor program, or as commands that control other computer applications. For example, a student who is unable to use a standard keyboard might use speech recognition technology to write a report in a word processing application such as Microsoft Word. He would do this by dictating words into a speech recognition program, and his words would appear on the screen in the word processor. Continuous speech recognition engines, now the most common form of speech recognition engines, have generally been used in two modes: dictation and command and control. In dictation mode, the speech recognition engine enables the user to enter data by speaking directly to the computing device. The user, in essence, dictates to the computer what he or she wishes to write (e.g., articles, letters, or email messages).

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Journal of Special Education Technology Most speech recognizers that support dictation mode are speaker-dependent, meaning that accuracy varies on the basis of the user’s speaking patterns and accent. To ensure accurate recognition, the application must create or access a “speaker profile” that includes a detailed map of the user’s speech patterns used in the matching process during recognition. This profile is normally created at the training stage. In addition to writing text, speech recognizers can help users navigate through a computer with the command and control mode. For example, a student who is unable to use a keyboard and relies on speech recognition to write a report also can use this technology to save files, print, or quit the program by using his or her voice. In command and control mode, the “grammar” (a list of recognized words) may be limited to the list of available commands, a much more finite scope than that of continuous dictation grammars, which must encompass nearly the entire lexicon. This provides better accuracy and performance and reduces the processing overhead required by the application. The limited grammar also enables speaker-independent processing, eliminating the need for speaker profiles or “training” of the recognizer. The capacity of a speech recognition engine may be judged by (a) what it can recognize, which is limited by the size of the recognizer’s grammar; (b) how accurate the recognition is, which is affected by the quality of input and the technology itself; and (c) how much training it requires or how speaker-independent it is. Today’s speech recognition technology can achieve an accuracy level above 90%; however, adequate training is needed to achieve this level of accuracy when using speech recognition in the classroom. Speech technology also has become more widely available. The technology has moved from large computers in speech labs to personal computers. Both the Macintosh and Windows operating systems include speech recognition engines in recent versions. Many stand-alone speech recognition computer programs also are available free or at low cost. Some of the most widely available dictation software on the market includes ViaVoice (IBM), Dragon (Dragon Systems), Voice Xpress (Lernout & Hauspie), and FreeSpeech98 (Phillips Speech Processing). A more comprehensive list of speech recognition software

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may be found at the Web sites for the Linux Documentation Project (http://www.tldp.org/HOWTO/SpeechRecognition-HOWTO/software.html), the University of Toronto’s Special Needs Ontario Window (http://snow. utoronto.ca/index.php?option=content&task=view&id =212&Itemid=103), and the NCTI TechMatrix (http:// www.techmatrix.org).

Speech Syntheses Speech synthesis, also known as text-to-speech, does the opposite of speech recognition. That is, instead of converting spoken language into text, it converts text into spoken language. A student who has difficulty reading might use speech synthesis to read information on the screen aloud. Speech synthesis is achieved with computer software programs that often are referred to as speech synthesizers. The quality of speech synthesizers may be assessed by a number of factors. First, how natural is the generated speech? That is, how human-like is the speech? While speech synthesizers normally can generate speech that is easy to understand, it may not sound natural. In many cases, it does not sound as smooth as human speech and lacks intonation, pauses, and other human speech characteristics that express emotions. Second, how efficient is the process in terms of computing power required and time needed for generating the speech? Some engines can produce speech that is more human-like, but require much more computing power and time. Some of today’s speech synthesis engines allow the users to select voices of different age and gender, or even different accents. It also is possible to control the speed, pitch, and duration of the speech, which enables the simulation of different moods and emotions. Speech engines and applications have become widely available as well. Both the Macintosh and Windows operating systems include speech synthesis engines. In addition, many stand-alone applications are available at no or little cost. A list of speech synthesis software also can be found on the Web sites for the University of Toronto’s Special Needs Ontario Window and the NCTI TechMatrix.



How Has Speech Technology Been Used for Individuals with Disabilities? The potential of speech technology to support individuals with disabilities has long been recognized. Attempts have been made to use this technology to assist individuals with disabilities primarily in two ways: assisting performance, and assisting learning. Applications of speech technology to assist performance have focused mainly on enabling individuals with disabilities to gain access to content that is otherwise inaccessible or to control devices that otherwise cannot be controlled. One of the most common applications is to enable visually impaired individuals to access written content through speech synthesis technology. The first application in this area was the Kurzweil Reading Machine in 1976. The world’s first print-to-speech commercial product, the Kurzweil Reading Machine enabled the visually impaired to “read” text. Subsequently, more sophisticated products were developed and deployed. Today, speech synthesis technology is commonly used to convert Web sites, email messages, and other written text into spoken language so that users who are unable to read because of physical disabilities or lack of literacy skills can access written content.

Reading Speech technology has not only been used to assist performance or enable access but also to directly assist learning for individuals with disabilities. The most obvious case to illustrate the use of speech technology for supporting learning of students with disabilities is still the print-tospeech application mentioned before. For a number of reasons, students with disabilities may not be able to access content in print, so enabling that access is critical. One of the most common uses is to enable students to read text. AspireReader™ is a good example of this type of application. Another example is the use of a speech recognition enabled digital dictionary such as the Quicktionary Reading Pen™, which can scan texts and read them aloud. The basic function of reading text aloud through speech synthesis also is available through most Macintosh and Windows computer applications (e.g., SimpleText for the Mac and Microsoft Word for Windows). The benefit of coupling text and converting text to spoken language can be powerful. JSET Volume 22, Number 3

So far, most studies have focused on the effectiveness of improving word recognition. Word recognition has been found to play an important role in reading comprehension, but students with learning disabilities often have difficulty decoding words (Forgrave, 2002). Speech synthesis technology allows the student to control the computer program to read back the text. Forgrave (2002) found the following: The immediate speech feedback allows students to correct their reading errors by clicking on a word they do not know in order to hear the correct pronunciation of the word. Text-to-speech programs reduce the frustration of inaccurate decoding for students with learning disabilities and allow for more complete comprehension of the text. (p. 123)

Several studies have demonstrated the benefits of speech synthesis technology for students with disabilities. In one study, when students with word recognition problems used speech synthesis software while reading stories on the computer, they demonstrated significantly improved decoding and word recognition skills (Higgins & Raskind, 2000). Similarly, when a group of students used books with a text-to-speech component, they showed improvement in word recognition and phonological decoding abilities (Barker & Torgesen, 1995). The addition of sound or speech feedback also has been found to help improve students’ spelling and word recognition scores (Lundberg, 1995), and to increase recall of text information (Montali & Lewandowski, 1996). Further, the use of speech synthesis technology has been used as a way to increase students’ motivation to read by presenting them with a more successful reading experience (Montali & Lewandowski, 1996). Specifically, when students with learning disabilities are motivated to spend more time reading, studies have shown increased reading skills and improved overall reading ability (Lundberg, 1995). A recent study found that the use of a simple device such as the Quicktionary Reading Pen™ can improve reading comprehension of students with learning disabilities (Higgins & Raskind, 2005). According to Forgrave (2002): Thus, the use of speech synthesis technology in middle and high school classrooms can assist students with learning disabilities in becoming more independent readers and can help them experience greater reading success. (p. 123)

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Writing Speech recognition technology also has been used to help students with disabilities with writing tasks. Thus, students can bypass their problems with lower order writing and typing skills by dictating their written work. Students with learning disabilities can produce text significantly faster using speech recognition than handwriting or typing (Graham, 1999). Also, stories written using speech-to-text dictation are longer, more complex, and contain fewer grammatical errors than those composed using other methods (Forgrave, 2002). Because speechto-text technology allows students to get their ideas down before they are forgotten due to slow typing speed, it helps students focus on the content of their writing rather than the mechanics of writing. The benefit of using speech recognition to assist with writing is not only short term. Studies have found this type of technology to have sustained remedial benefits. For example, Higgins and Raskind (2000) conducted a study that involved 39 students ranging in age from 9 to 18 years. They found that students who worked with voice recognition software showed significant improvement in reading comprehension, spelling, and word recognition scores compared to control group counterparts. Overall, the researchers concluded that both continuous and discrete voice recognition software can be used successfully across a range of age and ability levels, not only to compensate for students’ writing difficulties but also to improve their writing skills (Higgins & Raskind, 2000; Lewis, 1998; MacArthur, 1998). Students will write more with speech recognition technology because it enables them to produce text more easily and allows them to express themselves more efficiently. A study of the effects of speech recognition technology on writing for adults with learning disabilities also suggests that the technology can help improve the writing performance, but the improvement is contingent upon student characteristics and usage (Stodden, 2005).

General Literacy Currently, speech recognition technology is studied in more comprehensive learning environments to support the development of literacy. For example, Colorado Literacy Tutor, a comprehensive literacy program that uses computer-based learning tools incorporating human language technologies to teach students to read and compre-

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hend text, utilizes speech recognition extensively (Wise et al., in press). This system uses SONIC (Pellom, 2001), a large multilingual, continuous speech recognition system to enable automatic speech recognition. SONIC is integrated into one key component of Colorado Literacy Tutor, Interactive Books, to support such literacy activities as question answering, reading aloud, and summarizing stories. The read-aloud function in Interactive Books automatically follows the student as he or she reads the text aloud following the cursor. It flags unrecognized words by highlighting them at the end of a sentence. The student can click the word to hear it pronounced correctly for correction. Recognition accuracy for words spoken by young children in these applications is around 90%.

Issues and Directions: How Can Speech Technology Be Used for Individuals with Disabilities? The various research and development efforts to use speech technology to support individuals with disabilities have demonstrated the promise of speech technology, but they have been carried out on a very limited scale both in terms of conceptualization and implementation. This last section considers some of the limitations and proposes a framework for future research and development.

Limitations of Existing Efforts Existing efforts in using speech technology for students with disabilities have been limited in several ways. First, only a limited range of applications have been implemented. Most notably, until now, the use of speech technology has been limited to computer-based speech synthesis and recognition for reading and writing. Thus, there is little to no research on the use of other speechenabled devices, such as digital dictionaries that can scan and pronounce words. Further, limited attention has been paid to the potential benefits of the technology in other areas of learning such as science, mathematics, and social studies. Second, existing applications mostly have been direct uses of commercially available tools, which are secondary applications of speech technology. Very few applications have been developed specifically for students with learning or other disabilities. Finally, the applications


Journal of Special Education Technology so far have been limited to word processing and word reproduction. Speech technology has not been applied widely to other forms of learning tools, such as educational games and simulations, which could provide more interesting and promising results.

Recommendations for Future Research and Development Given the rapid development of speech technology and the growing availability of computing devices, the uses of speech technology to assist the learning of students with disabilities need to be reconceptualized. To start the reconceptualization process, we need to keep the following in mind: Speech technology, like most other information and communication technologies, was not, nor will it ever be, developed specifically for education, let alone education of students with disabilities. The effectiveness of speech technology for students with disabilities depends as much on its own capacity as on how it is used. Special educators need to reinterpret this capacity in the context of the learning needs of students in order to translate technological capacities into learning solutions. Thus, the first thing they need to do is to identify persistent and significant learning issues faced by students with disabilities and consider how they can be addressed with the use of speech technology. In other words, we should not limit ourselves to the functions of existing speech technology enabled products. Rather, we should consider speech technology as a platform or generic tool that can be and should be re-adapted, in much the same way we think about the printing technology. The limitations of speech technology can be an advantage for learning when conceptualized differently. Because speech technology is developed for various purposes, its limitation for one purpose may be an advantage for another purpose. For example, the machine-like voice of speech synthesis produced speech can turn out to be a great attraction to students when used properly. One possible use of speech synthesis is to have students assign different artificial voices to the different characters of their stories. Another meaningful use would be to attach the voice to simulated characters in chat rooms or learning games.


Speech technology should not be conceived of as a stand alone application, but as a building block of a learning system that incorporates many other technologies. Speech technology, on the surface, is essentially no more than input and output technology. That is, it is simply an alternative form of data input and output. With speech technology, the user enters and receives data in spoken rather than written language. But this technology is significant for individuals who are unable to manipulate (for input and output) written language or devices for input in written form because it enables them to interact with and gain access to devices and content that would otherwise be inaccessible. However, as with many other technological innovations, speech technology alone may not have the potential to benefit students directly. Its capacities can be realized or amplified much more when integrated with other technologies to provide educational solutions. For example, a player could give either a written or a spoken command to a computer program in a simulation and game environment, such as described in Shaffer (this issue). The computer program then would perform the command (Holland, Kaplan, & Sabol, 1999; LaRocca, Morgan, & Bellinger, 1999). To realize the capacity of speech technology, we also must consider seriously the current educational context of the learner. When thinking about how a technological innovation may bring educational benefits to the learner, we often focus on the direct link between the technology and the learner, ignoring the fact that the learner is already situated in a complex environment of human beings and other technologies (Zhao & Frank, 2003). This tendency limits our imagination about the potential benefits of technology for others involved in the child’s education, such as parents and teachers. It also may encounter massive resistance from the existing stakeholders. Our future research and development efforts should therefore go beyond what speech technology can do for the students directly, to include thinking about how it can be used to help parents, teachers, and other individuals. A good example is the New York City Department of Education’s effort to use speech technology to promote parental involvement by hosting a virtual call center that handles parents’ requests and homework hotline information in multiple languages (Hicks, 2004).

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Journal of Special Education Technology There is a growing need to systematically develop educational interventions using speech technology and study them rigorously. A retrospective look at the history of technology uses in education in general and special education in particular, yields two common sense messages. First, most of the efforts in applying technological innovations to education have taken place in isolation, have been fragmented, and have been repeated at very low levels. Over the years, we have seen many small applications that aimed at solving a discrete problem in education and that were then replicated by others. These small applications have not made much impact on education, although together they have consumed significant resources. Second, many of the previous efforts have been driven by technological capacities instead of educational needs. In other words, what seemed to motivate these efforts were new functions or features of technology instead of solid advancement in learning theories or persistent and significant educational issues.

low are the potential applications of speech technology in general. These applications push the technology to a more specific level with more specified functions, which makes it easier to consider how speech technology can be used in special education.

A look at the future of technology also yields two messages. First, technology will become increasingly available in different forms. There is little doubt that soon computer chips will be part of most daily appliances. This provides the infrastructure for deploying speech technology enabled educational applications. Second, speech technology will become more sophisticated and reliable and, more important, easier to integrate into other applications. Many speech technology developers have kits available for educators to repurpose their software for educational uses. Therefore, more coordinated efforts are needed now to conceptualize large-scale and high-impact educational solutions using speech technology instead of tinkering with small applications.

long lists enables a user to speak any one item from a list, or any command from a potentially huge set of commands, without having to navigate through several dialog boxes or cascading menus.

Potential Applications of Speech Technology So far we have reviewed the capacities of speech technology and some applications in education that may give us some sense of what speech technology is capable of and what we can do with it. But as mentioned before, because speech technology is a low-level generic technology, not a high-level, specific technology for education, our thinking about how to use it for students with disabilities must be framed by the educational needs of the audience and relevant educational theories. Instead of speculating about its potential uses for special education, listed be40

Speech recognition technology enables developers to include the following features in their applications:

• Hands-free computing may be used as an

alternative to the keyboard or as a way to allow the application to be used in environments where a keyboard is impractical (e.g., small mobile devices, AutoPCs, or in mobile phones).

• A more “human” computer, one users can talk to, may make educational and entertainment applications seem more friendly and realistic.

• Voice responses to message boxes and wizard

screens can easily be designed into an application.

• Streamlined access to application controls and

• Speech-activated macros allow a user to speak

a natural word or phrase rather than using the keyboard or a command to activate a macro. For example, “Spell check the paragraph.”

• Situational dialogs are possible between the

user and the computer, in which the computer asks, “What do you want to do?” and branches according to the reply.

Speech synthesis technology can be considered essentially as an alternative to digital audio recording. It can be used where digital audio recording is inadequate or impractical, such as when audio recordings are too large or too expensive to store on disk, when the application responses require only short phrases, when the application cannot predict what will be needed to communicate or alternative responses vary too much, and when the user prefers or requires audible feedback. Some examples of possible applications include:

• Text-to-speech can replace recorded sentences, saving memory space.

• The inevitably non-human quality of synthesized text-to-speech makes it ideal for character voices that are supposed to be robots or aliens.


Journal of Special Education Technology • If the application cannot afford to include

recordings of all the possible dialogs or if the dialogs cannot be recorded ahead of time, text-tospeech is the only alternative.

Conclusion In conclusion, speech technology holds great educational potential for students with special needs. Although the technology is far from perfect, research has suggested that proper uses of speech recognition and synthesis technologies can have positive compensatory impact on student literacy development and performance. What the special education field needs next are more systematic development efforts to fully realize the potential of speech technology while recognizing and avoiding its limitations. Future developments can benefit tremendously from a reconsideration of the relative role technology and human beings should assume in the process of using speech technology for educational purposes. A line from the film The Matrix provides great guidance in this regard: “Never send a human to do a machine’s job.” And vice versa: Never send a machine to do a human’s job. In other words, we must understand the capacity of technology and of human beings and work to ensure the two complement each other when designing new learning tools.

References Barker, T. A., & Torgesen, J. K. (1995). An evaluation of computerassisted instruction in phonological awareness with below average readers. Journal of Educational Computing Research, 13(1), 89-103. Forgrave, K. (2002). Assistive technology: Empowering students with learning disabilities. The Clearing House, 75(3), 122-127. Graham, S. (1999). Handwriting and spelling instruction for students with learning disabilities: A review. Learning Disability Quarterly, 22, 78- 98. Hicks, M. (2004). Gates launches Microsoft speech server. eWeek. com, March 24. Retrieved December 14, 2006, from http://www. eweek.com/article2/0,1895,1553678,00.asp Higgins, E. L. & Raskind, M. H. (2000). Speaking to read: The effects of continuous vs. discrete speech recognition systems on the reading and spelling of children with learning disabilities. Journal of Special Education Technology, 15, 19-30. Higgins, E. L., & Raskind, M. H. (2005). The compensatory effectiveness of the Quicktionary Reading Pen II on the reading comprehension of students with learning disabilities. Journal of Special Education Technology, 20(1), 31-40.


Holland, V. M., Kaplan, J. D., & Sabol, M. A. (1999). Preliminary tests of language learning in a speech-interactive graphics microworld. CALICO Journal, 16(3), 339-359. LaRocca, S. A., Morgan, J. J., & Bellinger, S. M. (1999). On the path to 2X learning: Exploring the possibilities of advanced speech recognition. CALICO Journal, 16(3), 295-309. Lewis R. B. (1998). Assistive technology and learning disabilities: Today’s realities and tomorrow’s promises. Journal of Learning Disabilities, 31, 16-24. Lundberg, I. (1995). The computer as a tool of remediation in the education of students with reading disabilities—A theory-based approach. Learning Disability Quarterly, 18(2), 89-99. MacArthur, C. A. (1998). From illegible to understandable: How word prediction and speech synthesis can help. Teaching Exceptional Children, 30(6), 66 - 71. Montali, J., & Lewandowski, L. (1996). Bimodal reading: Benefits of a talking computer for average and less skilled readers. Journal of Learning Disabilities, 29(3), 271–279. Pellom, B. (2001). SONIC: The University of Colorado Continuous Speech Recognizer (Technical Report #TR-CSLR-2001-01). Boulder: University of Colorado. Perfetti, C. A., & Marron, M. A. (1995). Learning to read: Literacy acquisition by children and adults (Technical Report TR95-07). Pittsburgh, PA: National Center on Adult Literacy. Shaffer, D. W. (2007). Epistemic games as career preparatory experiences for students with disabilities. Journal of Special Education Technology, 22(3), 57-68. Stodden, R. A. (2005). The use of voice recognition software as a compensatory strategy for postsecondary education students receiving services under the category of learning disabled. Journal of Vocational Rehabilitation, 22(1), 49-64. Wise, B., Cole, R., Van Vuuren, S., Schwartz, S., Snyder, L, Ngampatipatpong, N., et al. (In press). Learning to read with a virtual tutor: Foundational exercises and interactive books. In Kinzer, C. & Verhoeven, L. (Eds.), Interactive literacy education. Mahwah, NJ: Lawrence Erlbaum. Zhao, Y., & Frank, K. A. (2003). Factors affecting technology uses in schools: An ecological perspective. American Educational Research Journal, 40(4), 807-840.

Author Notes Yong Zhao is a University Distinguished Professor in the Department of Counseling, Educational Psychology, and Special Education at the College of Education, Michigan State University, where he also serves as the founding director of the Center for Teaching and Technology as well as the US-China Center for Research on Educational Excellence. Correspondence should be addressed to Yong Zhao, 115D Erickson, Michigan State University, East Lansing, MI 48824. Email to: [email protected]

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Technology at Home: Implications for Children with Disabilities Emily C. Bouck Purdue University Cynthia M. Okolo Michigan State University Carrie Anna Courtad Michigan State University Given the prominence technology holds in today’s schools and society, it seems crucial to explore its use and function in home environments for students with disabilities, particularly when considering everyday technology such as “smart” toys, computers, and communication devices. Unfortunately, little research or literature has been devoted to this issue. This paper reviews the literature on smart toys for children in general, and extrapolates what we have learned from smart toys and computer use in the home to children with disabilities. It suggests future directions for research, and proposes that the field of technology in the home for children with disabilities is wide open and clearly in need of study.

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esearch on the impact of technology on learning has been conducted almost exclusively in school settings. To date, little research has investigated children’s use of technologies and electronic learning toys within the home environment. Given the substantial amount of time students spend using technology out of school, and the increasing availability of lower cost, more sophisticated technologies for home use, the impact of these technologies on students’ learning and academic motivation becomes an increasingly critical question. The purpose of this article is to examine extant research about learning technologies used by students in home and other out-of-school settings. We are grouping a number of related types of objects under the label learning technologies, including smart toys and computers. And we include a range of ages, from young children to


adolescents. Where possible, we focus on research that has included students with disabilities. However, because the research base is so slim, we review research addressing the use and impact of these technologies when used out of school for students without disabilities and speculate about the implications of this research for students with disabilities. The focus of this article is on two types of technology that are commonly used in the home by children and youth with disabilities: smart toys and computers. We chose these technologies for the interactivity they offer to students and their parents and for the range of educational opportunities they afford. Although television and digital media such as cameras and music players also provide educational opportunities, they do not afford the same degree of interaction between the user and the tech-

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Journal of Special Education Technology nology. Furthermore, given the discussion of epistemic games offered by Shaffer in this issue, we do not address games or gaming for either computers or game consoles.

Smart Toys The Evolution of Smart Toys Play is an extremely important component of children’s development. Play has been shown to assist in development of self-confidence, collaboration, expression of emotion, and taking initiative, among other things (Ariel, 2002; Lindon, 2001). Toys and the play they can foster have long been considered a critical factor in children’s social, physical, language, and cognitive development. Thus, a variety of educational toys purport to teach children as young as infants the skills they will need to succeed in school and life. Some of these toys are designed to stimulate young minds, some are designed to provide practice with basic academic concepts or skills (e.g., cause-effect, letter recognition), and yet others are intended to provide more sophisticated interaction with their young users. Toys that claim to have educative benefits, or educational toys, have found their niche in American society (Paterson, 2003a). Products such as LeapFrog™ or Fisher Price™ learning toys quickly become the hottest toys on children’s wish lists and parents’ shopping lists (Carter, 2003). Educational toys, or at least their place in the industry, have come a long way since 1955 when novelty toymaker Louis Marx said, “I don’t go along with psychologists who want to sneak up on [children] and jam education into them through toys … [Only] spinster aunts and spinster uncles and hermetically sealed parents who wash their children 1,000 times a day give educational toys” (Cross, 2004, p. 20). Despite Marx’s cautions, parents are barraged with claims that the “right” toys and the “right” play can have dramatic effects on their child’s intelligence. Consider books such as Dr. Toy’s Smart Play: How To Raise a Child with a High PQ (Play Quotient) (Auerbach, 1998) or Raise a Smarter Child by Kindergarten: Build a Better Brain and Increase IQ by up to 30 Points (Perlmutter & Colman, 2006). Books like these and other media influences have parents running out to buy these products. Manufac-

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turers naturally respond to the demand. For example, Fisher-Price Toys indicated that in 1998, electronic toys made up 25% of its line (Buckleitner, 2001). In 2005, industry analysts estimated that more than 75% of toys introduced to children contained a microchip (Associated Press, 2005a). As technology has evolved, toys are not only able to interact with children in multiple ways, but they also react to the responses and preferences of the children who use them, and even “learn” with or from their young companions. Parents, or children themselves, are able to load information into a toy, via a computer, that personalizes children’s interaction with that toy. And, as technology has become less expensive, more families are able to afford smart toys, such as the I-Dog that costs about $20 and sits in the palm of one’s hand. Thus, toys have evolved from products with a few limited options that occur at random or in regular intervals to smart toys. Alfano (as cited in Kubin, 1999), a Ph.D. toy developer for Fisher-Price, purported that smart toys have their own intelligence and have been programmed with electronics that enable children to respond to the toy and the toy to tell the child if he or she has responded correctly. Shwe (1999) gave a simpler definition of a smart toy, stating that smart toys are ones that “leverage computing power” (p. 2). Finally, Roderman (2002) described them as toys with technology, ranging from software products to microprocessor-embedded plush toys. Industry analysts credit Furby, a must-have toy in the ‘90s that was able to “learn” English words, for pushing the smart toys movement forward (Maine, 2004). A newer version of Furby includes a technology dubbed emototronics, which reacts to words used by the child, such as “sad” or “hungry.” The merger of toys (or devices similar to toys) with computers has enabled the development of products such as LEGO Robotics (now LEGO Mindstorms™) and Logo Turtles (Papert, 1993). These types of toys are developed to encourage students’ creativity and problem solving, teaching academic content by virtue of hands-on, constructive experiences. More recently, Resnick (2006) and colleagues at the Massachusetts Institute of Technology’s Lifelong Kindergarten have developed Crickets, or miniature computers that enable children to animate their artistic creations with light, sound, music, and motion.


Journal of Special Education Technology Although toys such as Logo Turtles and Crickets have been used primarily in educational settings or research laboratories, most smart toys used by parents and their children are designed to teach or reinforce mathematics and literacy skills and include products such as LeapFrog™ products. Some toys, such as Time Tracker, are designed to prepare young students for better performance on specific tasks they will encounter in school—in this case, standardized testing.

advantage of a feature that would promote their interaction with the toy. The researchers noted that the educational value of the toy was no greater than that gained by many soft toys (i.e., fuzzy or stuffed toys), and they were doubtful that any short-term learning from interaction with the Actimates toys would be maintained (Luckin et al., 2003).

Many toys incorporate music, either to boost their “smartness” or to make them “educational toys.” The toy industry’s use of music, particularly classical music, stems from the Mozart Effect, which is still a popular notion among many parents and early childhood educators. Paschal (2005) contended that the Mozart Effect resulted from the media’s misrepresentation of a 1993 study that demonstrated a relationship in college students between short-term spatial reasoning improvement and briefly listening to Mozart. This study has subsequently been discredited by about 20 other research studies. Despite the widespread addition of music to many toys and anecdotal observations that music can be stimulating and relaxing, evidence is lacking regarding its causal effect on children’s intelligence. Nevertheless, as we will discuss later, music may offer some advantages to children with disabilities.

Despite their popularity, a number of concerns have been raised about the use of smart toys in the home. Medical professionals and early childhood specialists have expressed concern about the prevalence of educational toys in children’s lives and their impact on children’s play (Paterson, 2003b). Experts contend that educational and more technologically advanced toys decrease the need for parents and young children to interact together around the toy, which is believed to be detrimental to preschool children (Paterson, 2003b). Thus, psychologists, child development specialists, and others specializing in children suggest that classic toys such as puzzles, blocks, and books that require the caretaker and the child to read or interact together may best encourage cognitive and emotional development. Dwight (1999), in Parenting magazine, argued that technological sophistication does not make a toy a better or a more valuable playmate for a young child. In fact, Dwight suggested that toys for young children should offer the opportunity for children to invent their own play activities, as opposed to following pre-set activities and games often embedded in educational toys.

Research focused on young children’s use of technologybased educational toys has found that engagement with smart toy not only allows children to practice their fine motor skills but also begin to facilitate cause-and-effect relationships (Jones & Liu, 1997). However, another study examining young children’s use of smart toys did not produce positive outcomes. Project CACHET (Computers and Children’s Electronic Toys) examined Microsoft Actimates Arthur and D.W., from the book and television series Arthur (Luckin, Connolly, Plowman, & Airey, 2003; Plowman & Luckin, 2003). These smart toys were popular in the late 1990s but were discontinued in early 2000. A child could interact with Actimates via computer. A home study, conducted with 12 children ages five and six, revealed that children were not impressed with the toys’ interaction and maintained interest in them for only about two weeks (Luckin et al., 2003). The researchers concluded that the toy manufacturer’s claim that these toys were “a child’s computer learning buddy” was unrealistic, particularly as children rarely used the help feature. Thus, they did not take full


Concerns About Smart Toys

Pediatricians also have warned parents not to fall prey to the claims of toy manufacturers regarding the intellectual enhancement of educational toys (Morantz & Torrey, 2003). They caution that no scientific evidence exists to support claims that any toy is either “necessary or sufficient for optimal learning” (Glassy, Romano, & the Committee on Early Childhood, Adoption, and Dependent Care, 2003, p. 911). Others have suggested that the demand for educational toys by parents is based more on “wishful thinking than hard evidence” (MacDonald, 2003, p. 12). Even professionals who support educational toys acknowledge that they are not likely to increase intelligence (MacDonald). Many experts agree that claims of smart toys’ benefits are just that, claims consisting of sales pitches and marketing messages. They

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Journal of Special Education Technology argue that there are no data to support claims of IQ gains (Carroll, 2004; Weiner, 2004). Others have asked whether, when smart toys are added to children’s play, “real” play and its advantages suffer (Plowman, 2004). Bergen (2001) argued that smart toys place children in the reactor role, as opposed to the role of actor. Research has shown that children’s pretend play, in which a child is the initiator and constructor of action, is linked to their ability to creatively solve problems (Alliance for Childhood, 2004). Hirsh-Pasek, Golinkoff, and Eyer (2004) cautioned that, instead of creativity, smart toys encourage memorization and emphasize convergent thinking, which may be detrimental to children’s views about learning and themselves as learners (Carroll, 2004; Weiner 2004). Educators, health professionals, and parents alike also have expressed concern that educational toys will speed up childhood, contrary to the recommendations of many developmental experts that children develop at their own pace. Industry analysts note the “age compression” in children’s use of toys, with interest in certain types of toys, such as Barbie™, occurring at earlier ages (Barnes, 2001). Joanne Oppenheim (2003), a Today Show contributor and child development expert, stated that while manufacturers claim that their smart toys may help children learn faster and achieve more, they are neglecting to consider children’s developmental trajectory. Oppenheim contends that smart toys tend to push academic skills at earlier ages, before children are developmentally ready to learn them, making skill acquisition more difficult, and perhaps damaging a child’s self-esteem or sense of self as a learner. In response to such criticism, toy manufacturers claim they are only responding to the preferences of young users, who are becoming more sophisticated and expecting toys with greater interaction and sophistication (Associated Press, 2005a). What these consumer preferences mean for actual intellectual development is unclear. Brain research has confirmed that the brain develops at a particular rate and cannot necessarily be “sped up” to mature at an earlier age. Furthermore, there is concern about play with certain types of toys among children who are not emotionally or morally mature (Alliance for Childhood, 2004). This is particularly true for young children, as the American Academy of Pediatrics has recommended that children under the age of two not have any exposure to screen media.

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Smith (2002) warned that, before the ages of two to three years, children do not use toys for learning. Rather, at these early ages, children explore toys in ways that can provide sensory input and help develop cause and effect and motor skills. He concluded that claims regarding toys’ benefits on learning for children under two years of age are not supportable because children have not yet developed the physical, cognitive, and visual abilities that would enable these toys to provide true learning experiences. Despite the controversy generated by smart toys, surprisingly few researchers have examined children’s use of any type of educational toy in the home or out of school (Bergen, 2001). Experimental research to isolate the impact of various features and types of uses would be extremely difficult to implement in home settings (MacDonald, 2003), and research about how toys could be used to improve students’ readiness for school or achievement is extremely slim (Roderman, 2002). Also lacking are empirical data that could help ground recommendations to parents about the potential match between types of toys and the needs and learning characteristics of their children (Roderman). As Seiter (1999) pointed out, contrary to the concerns expressed above, toys play an important role in children’s culture, and parents can play an important role in helping children use these toys in ways that facilitate their development. Plowman and Stephen (2003) suggested that the harmful effects of technology toys on children are over-exaggerated, particularly any concerns about a toy inhibiting children’s interactions with others. Plowman and Stephen remind us that there is not only an absence of research about the advantages of toys—there is also an absence of research about their disadvantages. Thus, educators and parents should be cautious in their conclusions that smart toys may have negative effects.

Smart Toys and Children with Disabilities Although the extant research is meager, several studies have examined the impact of toys, in general, on social and cognitive outcomes for students with disabilities (Swanson, 2003). The conditions under which children use those toys are an important variable in these studies. Reinhartsen (2002), in a sample of children with autism, found that letting children choose their toys, rather than working with toys provided by the teacher, led to greater


Journal of Special Education Technology engagement and fewer problem behaviors in two of the three who participated in their study. Malone (1999) found that children with developmental disabilities engaged in more sophisticated play for a longer duration when they used toys at home, compared to use of toys in free-time school-based play. Other researchers have found that children with disabilities engage in more social interaction when they play with toys such as blocks or balls that facilitate social interaction, rather than toys that are better suited to individual use (Kallam & Rettig, 1991; Rettig, Kallam, & McCarthy-Sahn, 1993). Ganz, Cook, and Earles-Vollrath (2007) studied toys as a form of incidental teaching for children with mild communication disorders. They found that showing children a preferred toy and then progressively prompting their verbal interactions about that toy improved their communication.

school environment at various ages. One study examined inclusive early childhood education programs (i.e., preschool, kindergarten, first grade). The study found that preschool aged students with disabilities gained 35% in their letter sound identification skills and experienced a 9% growth in their letter name knowledge, whereas kindergartners and first graders showed an increase of 22% in their identification of letters sounds and 29% in the recognition of letter names (Leapfrog Schoolhouse, 2006). Similarly, during a three-week intervention using LeapFrog products, students with disabilities in grades K-3 improved performance in letter sound identification (44%), rhyming (87%), and initial sound fluency (43%) (LeapFrog Schoolhouse, 2005). While these results sound promising, they must be viewed with caution until they are replicated by an independent source.

Although research is lacking regarding the application of smart toy use in the home for children with disabilities, toys that develop school readiness and academic skills could be beneficial. For example, children’s early phonological awareness is a predictor of early literacy (Torgesen, 1998), and thus, playing with toys designed to promote phonological development may promote early literacy (Lonigan, et al., 2003). Smart toys designed to resemble popular game systems have the potential to strengthen fine motor skills and improve manual dexterity for students with disabilities, particularly those with cerebral palsy (Swanson).

In summary, the research to date on the use of smart toys with children who have disabilities is promising, but by no means complete. Although many articles have been written advising parents about toy selection for their child with a disability, much more research is needed before we can draw firmer conclusions about the effects of smart toys (or toys in general), the conditions under which they will be most beneficial, and the manner in which parents can select smart toys that are optimal for their child.

Similarly, activities embedded in music within educational toys may increase the communicative behavior of young children with disabilities (Braithwaite & Sigafoos, 1998). Swanson (2003) suggested that music is both a motivator and a learning experience for students who are blind or who are diagnosed with autism, and thus, musical smart toys may advantageous for students with these disabilities. Swanson also contended that smart toys that involve light or other visual processes, such as Sing and Smile Pals by V-Tech, are easily activated and provide benefits to children with disabilities. In fact, VTech toys have been found to be widely accessible for children, particularly young children, with special needs (Swanson).

Computers

Research on the use of smart toys in the school environment has reported positive results for young children with disabilities. LeapFrog Schoolhouse™ (2005, 2006) conducted research examining the use of its product in a


Evolution of Computer Use in the Home Computers and the activities they afford are important aspects of learning in home environments. Computers are playing an increasingly important—and timeconsuming—role in students’ lives, as children steadily spend increasing amounts of time playing with computers and/or video games, communicating with others on the Internet, and researching information (Stranger & Gridina, 1999; Subrahmanyam, Kraut, Greenfield, & Gross, 2000). According to the 2003 United States census (U. S. Department of Commerce, 2005), more than 60% of homes in the United States have a computer, and 55% of households have access to the Internet. The frequency of home computer use continues to increase at a rapid pace. In a representative sample of 8-to-18 yearolds, Rideout, Roberts, and Foehr (2005) found that chil-

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Journal of Special Education Technology dren and youth spend about an hour a day on computers outside school. Eight-six percent of the participants in this survey lived in homes with a computer, 31% had a computer in their bedroom, 74% had Internet access at home, and 31% had high-speed Internet access. Older children (aged 13-17) often use computers for schoolwork in the home (Turow, 1999); however, social uses of computers, particularly instant messaging (IM) and social networking online, are rising sharply (Lenhart, Madden, & Hitlin, 2007). Plowman and Stephen (in press) contend that children’s interaction with computers can offer three important advantages: (a) development of students’ dispositions to learn, (b) increases in students’ motivation and engagement, and (c) knowledge of the world, including the academic content that students are expected to master in school. Furthermore, as opposed to the generally limited use of computers in the classroom (e.g., Cuban, 2001), children are more likely to have longer and less interrupted periods of time on a home computer (Plowman & Stephen, 2003). However access to computers at home remains affected by income and ethnic factors, a phenomenon that has been termed the digital divide. Despite commentary that the digital divide is closing, particularly in schools, it still exists in the home. For example, the 2003 census (U. S. Department of Commerce, 2005) also revealed that in households with an income under $25,000, only 41% owned a computer; whereas 92% of families with income over $100,000 owned at least one computer. Children from lower socioeconomic status families are only 0.36 times as likely to own a computer as are children from higher socioeconomic status families. Differences among educational levels are even starker, with home computers in only 28% of households in which the head of household has less than a high school education (U. S. Department of Commerce, 2005). In addition, Rideout and colleagues (2005) found that whereas about 80% of white children and youth can access the Internet from home, only about 67% of Hispanic and 60% of black youth have home Internet access. Moreover, the documented digital divide over access in the home setting is independent of age, as distinct differences have been found for both older children (Eamon, 2004) and young children (Judge, Puckett, & Cabuk, 2004). Even children as young as two years of age are using computers in the home on at least a sporadic basis (Stranger &

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Gridina, 1999). Roderman (2002) discussed “lapware,” products that introduce infants and toddlers to computers and computer accessories. Lapware has been designed for infants as young as six months. The manufacturers claim that introducing children at an early age to computers will give them not only an advantage in school but also in their future work places. In a nine-month period, close to one million dollars were spent on software for infants and over ten million for toddlers (Roderman). As computer availability and use has increased among children and teens, so has their use of the Internet, email, instant messaging, and other forms of social networking. While earlier uses of home computers for young children focused on educational and entertainment software, current uses are more diverse and sophisticated. The Internet has replaced the traditional mode of research that was once formally conducted in the library, giving not just scholars, but also students and their parents, access to more information with greater ease (Lenhart, Simon, & Graziano, 2001). Adolescents also commonly use the Internet as a form of communication and expression. Gross (2004) described the Internet as a new social environment, offering expanded access to a social community, whether it is local or global. Social networking sites, such as Facebook and MySpace, are one of are the fastest growing segments of the Internet, and social networking has become one of its most prevalent uses by young people. Fifty-five percent of 936 teens aged 12-17 surveyed in a recent study visited a social network site once a day (Lenhart & Madden, 2007).

Concerns About Children’s Computer Use Similar to the concerns outlined about smart toy use, educators, pediatricians, and child development specialists have spoken out about the negative consequences of early and consistent computer use (Rufus, 2004). The Alliance for Childhood (2004) report cautioned that computer use among children is a poor substitute for more creative and physical activities. The visual stimulation offered by computers may be more salient than the images children create for themselves through creative play and art (Vail, 2001), leading children away from some of the learning environments that are known to support cognitive and physical development. Medical risks also may accompany increased computer use among children. Extensive computer use can lead to physical problems and injuries, such as eyestrain; wrist, neck, and back pains; poor pos-


Journal of Special Education Technology ture; and repetitive stress injury. In addition, pediatricians and others have expressed concerns about obesity caused by a sedentary childhood (Alliance for Childhood; Healy, 1998). Finally, Plowman (2006) argued that computers, given their ergonomic unsuitability, may not be the best technology tools for young children. She urged preschool educators to expand their ideas of Information and Communication Technology (ICT) to digital still and video cameras, mobile phones, electronic keyboards, and computers developed for young children. Perhaps the most widely cited critic of childhood computer use is Jane Healy. In her 1998 book, Failure to Connect, Healy raised concerns about the dangers that computers pose to developing minds. In particular, she noted that young children learn best through their senses and in interaction with other humans, such as parents and siblings. Replacing those experiences with a computer amounts to replacing a three-dimensional with a one-dimensional experience in which children are exposed primarily to symbolic rather than human interactions. Healy argued that children under seven years of age should not use computers. Other authors (e.g., Roderman, 2002) have expressed similar concerns, noting that computer programs provide primarily nonlinguistic and visually distracting input and, thus, may cultivate a generation of learners who prefer action and distraction to reflection and contemplation. Researchers have speculated that increased computer use also will have negative social consequences (Roberts, Foehr, & Rideout, 1999). Social difficulties are purported to occur when children spend too much time playing video games, thus hampering their interpersonal skills and friendships. Academic risks also have been associated with home computer use. Similar to research citing the negative relationships between television viewing and academic performance, recent research has shown that elementary school children with computers in their bedrooms scored lower on language arts and math tests (Associated Press, 2005b). Teachers have noted that computer activities have changed the process by which children write and their attention to the mechanics of writing process and the mechanics of writing, as real-time writing through email and instant messaging is not held to the same standards


as more formal writing (Wu, 1997). Parents and educators have expressed dismay at the nonstandard forms of language that are proliferated by children who use the Internet for communication (Wu). However, Baron (2005) reminds us that adolescence has typically been a period of “linguistic and behavioral novelty” (p. 30), and that forms of communication on the Internet (and on cell phones and other communication devices) are just a variant on a perennial cultural practice. Baron argued that the only real danger in the use of nonstandard forms of communication occurs when teachers and parents abdicate their role in giving students sufficient exposure to and instruction in the conventions of written and oral communication. Undoubtedly, the biggest concern about computer use among children and teens is exposure to inappropriate, even dangerous, content and experiences. A National School Boards Foundation (2000) survey found that children and parents view the Internet as a positive force but also have concerns about exposure to unacceptable and/or violent information. Well-publicized incidents of sexual predators soliciting young children on the Internet raise grave concerns about allowing children and youth unimpeded access to unknown others (Hansen, 2006). Cyberbullying, or harassment and verbal abuse by peers, is another concern (Stover, 2006; Strom & Strom, 2005). Furthermore, young children appear unable to distinguish content from advertisements, thus believing that most all information they find on the Internet is credible (Gilutz & Nelson, 2002; Henke, 1999). A final concern relates to parental expectations for the benefits of home computer use. As is the case with smart toys, parents are bombarded with messages about the importance of providing their children with a home computer. In a survey by the American Association of School Administrators, parents placed greater value on computer literacy than social, relational, and emotional skills for their children (Roderman, 2002). Similarly, in the National School Boards Foundation (2000) survey, parents’ most frequently cited reason for purchasing a home computer was education. Nevertheless, it seems that the social, communicative, and leisure activities afforded by computer may far outstrip educational uses in the home (Appel, 2006).

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What Does Research Tell Us About Home Computer Use? Researchers have investigated many of the issues raised above and have offered evidence to quell much of the concern expressed by educators, psychologists, medical practitioners, and parents. With regard to computers’ effects on children’s play and creative development, several researchers found that computers promoted more cooperative and dramatic play or engagement in imaginary activities than did puzzles and blocks (Kim, et al., 2003; Villarruel, 1990). Researchers also found that when children engaged with computers, they had less parallel play than with typical or more traditional toys (Judge, 2001). In addition to promoting play, computers can develop important technology literacy skills and introduce young children to tools that will shape their future (Plowman & Stephen, in press; Roderman, 2002). Subrahmanyam et al. (2000) suggested that computers, particularly computer games, can support computer literacy by helping children read, visualize in three dimensions, and track multiple images simultaneously (also see Carlson, 2003). Flynn (1994) found that computer use at home might involve similar skills as those tested in nonverbal intelligence tests, including logical and decision-making skills. Educational games also have been linked to improvement in communication, problem-solving abilities, alertness, and vision. Furthermore, teachers have reported that students who played educational computer and video games had improved achievement in mathematics, spelling, and reading (Rufus, 2004). Nichols (1992) and Linn and Dalbey (1985) found significantly higher scores on computer literacy tests for high schoolers who used educational software than those who did not. Research also has supported academic and intellectual benefits of computer use (Subrahmanyam et al., 2000). Using time-diary data from a national sample of young school-age children, Attewell and Battle (1999) found modest benefits for use of home computers on three tests of cognitive skills and one measure of self-esteem. Further, they did not find deficits in reading, sports, and play when computers were used up to eight hours per week. However, more than eight hours a week of computer use was associated with decreases in those activities and outcomes.

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Other educational benefits include students’ use of home computers as a study aid. Students themselves have identified access to the Internet as essential for completing school assignments (Lenhart, Simon, & Graziano, 2001). Lenhart and colleagues found that almost 95% of students used the Internet for help in schoolwork, and 74% of these students stated that they used primarily Internet sources for the last research paper they had written. Students cited the speed and ease of using the Internet as the reason for relying so heavily on this source for research. Students specifically named the efficiency of typing key words into an Internet search engine as opposed to “aimlessly” walking around the library looking for resources. In addition, older teens reported that they use instant messenger and email to contact peers about homework problems. About 40% of teenagers also reported that they communicated with their teachers through instant messaging and email regarding homework (Jenkins, Clinton, Purushotma, Robinson, &Weigel, 2006). Research about social isolation and technology’s inhibitory effects on social development is inconclusive (Attewell, Suazo-Garcia, & Battle, 2003). For example, Phillips, Rolls, Rouse, and colleagues (1999) and Colwell, Grady, and Rhaiti (1995) found no difference in social interactions between student computer users and non-users. They proposed that computers increase social interactions among children. Rideout and colleagues (2005) found that children and youth who reported spending the most time with media (including computers, TV, and digital music players) reported spending more time with their parents and friends, were more physically active, and had more hobbies than peers who spent less time with media. Despite the disadvantages and dangers associated with social networking, it is difficult to argue that these sites have not offered students expanded access to social interaction. Jenkins and colleagues (2006) indicated that the opportunities for interaction and the creation and sharing of digital content on the Web permit students to develop new affiliations, offer new means of expression, facilitate collaborative problem solving, and reverse the flow of media from centralized entities to more diffuse and democratic participation. Furthermore, these types of Internet sites may be viewed as cultural tools, stimulating new cultural norms that are quickly passed on to subsequent generations of users (Greenfield & Subrahmanyam, 2003).


Journal of Special Education Technology Finally, recent studies show that concerns about the dangers of social networking may be exaggerated. For example, about 60% of teens surveyed by Lenhart and Madden (2007) said they allow only friends to view their online profiles, hence limiting access to personal information that might be used by predators or other individuals with harmful intentions. In another study (Hindaju & Patchin, 2007), researchers randomly sampled 1,500 teens’ MySpace profiles, and found that 91% did not include last names and the majority did not include other personal information (e.g., school attended or email/IM addresses). The researchers could not view about 40% of the profiles sampled because the teens who created them made them available only to friends.

Home Computer Use for Students with Disabilities Although a large body of research has been collected on the impact of various technology-based applications and interventions on the achievement, social interactions, communication, and functional skills of students with disabilities, most of this research has been conducted in school settings. There is a paucity of research about computer use in the homes of families of children with disabilities. However, one can speculate that home use of computers could offer many benefits to the students with disabilities and their families. Yet, researchers have shown disparities in Internet use among individuals with and without disabilities are as great, if not greater, than the differences among educational levels and ethnicities attributed to the digital divide (Kaye, 2000). A plethora of Web sites offer programs that help students practice academic skills and support and contain information to support homework and other school tasks (e.g., http://mathforum.org/dr.math/; http://school.discovery.com/homeworkhelp/bjpinchbeck/). Sites such as Literacy Access Online (http://literacyaccessonline.com) provide online tools and materials that parents can use to facilitate their child’s literacy development. However, we could not locate research that either documented the extent to which students with disabilities use these types of sites, or the effect of these resources on student achievement.


A large volume of Web sites also exist to assist children by organizing, guiding, and/or simplifying the process of finding relevant information about a topic, thus facilitating students’ independent research (e.g., http://www. ala.org/ala/aasl/schlibrariesandyou/k12students/k12students.htm). As stated above, adults and children find it more convenient to search for a topic within an Internet search engine on a computer in their home than to go to the library (Lenhart, Simon, & Graziano, 2001). Internet search engines also have been attributed with helping promote students’ research skills (Holum & Gahala, 2001). Resources on the Internet may similarly assist students with disabilities. On the other hand, many of the sites commonly used may pose challenges to students with literacy, sensory, and cognitive disabilities. As discussed above, email, instant messaging, and social networking encourage different types of communication than does traditional print (Wollman-Bonilla, 2003). Some have suggested that the authentic nature of social networking, as well as the desire to communicate with peers, has helped students view writing as a desirable and motivating task. Similarly, loosening the restrictions of traditional writing may open up new opportunities for students with disabilities to write more extensively and communicate more frequently in writing. Thus, technology-based communication opportunities might create a strong foundation for writing and for instruction that takes advantage of students’ experiences and interests (Wu, 1997). The Internet also provides students access to electronic books and other online texts from home, a factor that could support the development of reading skills and access to school textbooks and trade books. Electronic books or online texts have text enhancements, such as providing definitions of words, background information, and images or illustrations. They may also include embedded speech, thus helping students with basic word recognition and extending comprehension (Anderson-Inman & Horney, 1998). Given the growing availability of inexpensive and free text reading applications, students with disabilities can listen to their online textbooks and other school-based reading materials at home. Furthermore, many trade books can be downloaded from sites such as iTunes, Audible, Amazon, and Audiobooks, enabling students with disabilities to hear books on their home

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Journal of Special Education Technology computers or on their digital music players. Thus, home computer use can help students who have difficulty reading gain access to the same information as other students (Holum & Gahala, 2001; Okolo, 2006).

Discussion and Conclusions Out-of-school use of smart toys, computers, and mobile information and communication devices holds great potential for students with disabilities and their families. It is not difficult to envision ways in which technology applications and devices can reinforce and enhance learning, communication, social opportunities, and leisure activities for children and youth with disabilities (Hasselbring, 2001). However, research on their use in the homes of children in general, and children with disabilities in particular, is woefully inadequate. Thus, conclusions about dangers and advantages of these technologies rest more on opinion and rhetoric than actual data. Their use, or lack thereof, is more often subject to market forces than the needs and characteristics of their users. And while educators, researchers, and parents may have strong beliefs about the impact of these tools on children, additional research is needed. What we do know is that technology is here to stay and that it will increasingly affect the lives of all children, including those with disabilities. It is probable that the most substantial uses of technology will occur in the home, given the continuing challenges that schools face in integrating technology into instruction (Cuban, 2001). We also know that children with disabilities have an increased opportunity to access powerful technologies with the potential to compensate for disabilities and, perhaps, accelerate learning. Because these technologies are so common, they are not stigmatizing, as older technologies (such as audiotapes of textbooks) often were. Also, the use of computers, smart toys, and mobile information and communication devices in the home enables students with disabilities to receive information and work in ways that are private and subject to less scrutiny by peers or others, perhaps allaying concerns about revealing their disabilities. Technology designed for personal use by the general public is increasingly being adapted for individuals with disabilities, such as cell phones, PDAs, etc. (Howard, 2004). For example, a cell phone can be used to make 52

phone calls, but also can be equipped with GPS software that not only allows one to track where they are in space, but also to assist a student with a disability in finding a bus station, a police station, and other locations in the community. In a more academic sense, a cell phone also can provide students with games, quizzes, word wizards, drills in math and vocabulary, current affairs information, and practice quizzes in math, reading, and grammar to prepare for college admissions tests (Schevitz, 2004). A PDA can assist with organization and management, but also can incorporate augmentative and alternative communication software that can allow a student to communicate through voice output in the home, community, and school environment. These common devices provide students with and without disabilities access to educational, organizational, social, and recreational opportunities, presented in a “socially-acceptable” and nonstigmatizing manner as well as one that is accessible to a wide age group (CBS, 2005). These are interesting times in which to live, and undoubtedly technology will continue to shape our ideas and practices related to education, communication, employment, and leisure. Young people will be on the forefront, if not the leaders, of these changes (Lenhart, Madden, & Hitlin, 2007). Prensky (2001) contended that today’s children are digital natives—radically different from their predecessors and deserving of a drastically different educational system. As educators, parents, and other professionals, we will be challenged to re-examine and, perhaps, change our ideas and practices related to children’s development and education. For example, some scholars caution that blaming technology for the death of childhood constrains us from understanding and making the best use of the opportunities it affords (Buckingham, 2000, cited in Plowman & Stephen, 2003). Furthermore, as boundaries blur between entertainment and education, we must give further credibility to applications such as epistemic games (Shaffer, this volume) and determine how to capitalize upon their possibilities within the classroom (Plowman & Stephen, 2003). It also seems certain that home technology use for individuals with disabilities is at least somewhat limited by the lack of more responsive interfaces and applications (Stock, Davies, Davies, & Wehmeyer, 2006). Internet accessibility has been a major concern of people with disabilities and their service providers. Albeit not fully resolved, significant progress has been made on issues


Journal of Special Education Technology of the Web and translation of print resources into more accessible formats. However, access to content for individuals with cognitive disabilities, and the translation of multimedia resources into comprehensible forms for individuals with visual and hearing impairments, require concerted attention. Furthermore, more user-appropriate interfaces than keyboards may be of assistance to young children with disabilities. Plowman and Stephen (2003) urged developers to move past screen-based technologies and consider tangible or touchable interfaces for young learners. Other technology issues that require the field’s attention include the relative advantages of miniaturization, which increases the portability of a device, versus the accessibility complications of smaller devices.

Suggested Directions for Future Research Given the shortage of data, it is not difficult to articulate a number of research questions that could be asked about home technology use for students with disabilities. Despite the recent effort to understand how children and youth use technology and their perceptions of that use (Lenhart & Madden, 2007; Lenhart, Madden, & Hitlin, 2007; Rideout, Roberts, & Foehr, 2005), current publications and reports contain virtually no information about students with disabilities. How do students with disabilities use technology at home? Do they make extensive use of email and instant messaging, social networks, or Internet-based learning and research sites? What impact can these opportunities have on achievement and social interaction? Are there differences in use between students with and without disabilities? If so, what can parents and educators do to ensure there is not a third digital divide? What support, guidance, and instruction from parents, guardians, siblings, or service providers is needed to optimize the use of technology in the home? How do we build technology use into individualized family service plans? How can educators capitalize on technology’s motivational and compensatory factors to make learning more efficacious and motivating? How do teachers bring the culture of students’ out-of-school uses of technology as a foundation into the classroom? Answers to these and other questions must be addressed by research that meets standards for quality and credibility (Gersten & Edyburn, this issue). Such diverse questions are best tackled through multiple research approaches and methods, some of which may be unfamiliar to the special education community. For example, social


network theory may be a fruitful approach for better understanding how students with disabilities interact with others as they use technology (Sylvan, 2006). Multi-level analyses, available through techniques such as hierarchical linear modeling and structural equation modeling, offer tools for better understanding the impact of the various factors that influence students’ technology use. Longitudinal research is needed to assist educators and parents in supporting students’ technology use over time. In addition, qualitative research will play an important role in providing the rich descriptions and analyses of how and why students with disabilities use technology in the ways that they do. Finally, scholars such as Kinder (1991) remind us that we need to frame future research not only in terms of the impact of specific, individual technologies, but under conditions of transmedia intertextuality. She contends that researchers and practitioners should pay more attention to the convergence of different forms of media and consider students’ total media experiences in our future conceptualizations about technology use and its impact on student outcomes. These ideas may be of particular importance for students with disabilities who may benefit most from the new opportunities, compensatory features, and authentic learning experiences that can occur through technology.

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Author Notes Emily C. Bouck is an Assistant Professor in the Special Education Program, Department of Educational Studies, College of Education, Purdue University. Cynthia M. Okolo is a Professor in the Department of Counseling, Educational Psychology, and Special Education, College of Education, Michigan State University. Carrie Anna Courtad is a doctoral student in the Department of Counseling, Educational Psychology, and Special Education, College of Education, Michigan State University. Correspondence should be addressed to Emily C. Bouck, BRNG 5146, College of Education, Purdue University, 100 N. University Street, West Lafayette, IN 47907-2098. Email to bouck@ purdue.edu



Epistemic Games as Career Preparatory Experiences for Students with Disabilities David Williamson Shaffer Department of Education, University of Wisconsin-Madison and the National Center for Technology Innovation This paper looks at how computer- and video-based epistemic games can help provide career preparation experiences for young people. Epistemic games are a simulation of professional training in game form. As such, they help players try on or assume different professional identities and learn to think and act like professionals in that community. First, journalism is explored as a community of practice. Second, a study of a journalism-based epistemic game called science.net with middle school students is shared, demonstrating the learning potential of this approach. The value of a practicum for career preparation and transition planning is discussed, and readers are challenged to consider how epistemic games might benefit students with disabilities who are preparing to enter the work force.

T

rying out jobs can help prepare young people to become productive adults, and may give youth with disabilities a chance to develop and demonstrate skills and competencies that school and home do not provide (Cameto, 2005). More broadly, career preparatory exercises—career awareness and exploration activities, employability skills training, and actual work experiences—help young people choose careers that meet their individual interests, needs, and abilities (Hughes & Karp, 2004; Lapan, Gysbers, & Sun, 1997). This article looks at how computer- and video-based epistemic games can help provide such experiences, and challenges readers to consider how this approach to learning might benefit youth with disabilities. The Individuals with Disabilities Education Improvement Act (IDEA, 2004) encourages preparation for postsecondary employment of students with disabilities by mandating transition services for students, beginning at age 16 or before. Although the most commonly stated primary transition goal for secondary students with disabilities is finding competitive employment (Blackorby &


Wagner, 1996), students with disabilities are more likely than students without disabilities to be unemployed and receive lower pay (Dunn, 1996; National Council on Disability, 2003). Furthermore, many students with disabilities exit high school feeling ill prepared to enter the workforce (U. S. Department of Labor, 1991). Often they struggle through high school and do not develop the higher order thinking skills that are required of most workers (Baker, Kameenui, & Simmons, 2002). They may have trouble envisioning themselves as members of a profession and may develop unrealistic expectations of what will be required. Computer- and video-based epistemic games may be able to support students in the transition process by providing them a platform for participating in structured and scaffolded career preparatory experiences. To present the potential of this approach, we describe science.net, a game developed by researchers David Hatfield and Alecia Magnifico at the University of Wisconsin (see Shaffer, 2006). In science.net, players become journalists, reporting on scientific and technological breakthroughs for an online news magazine. Along the

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Journal of Special Education Technology way, they learn about science and its impact on society, become better writers, and come to see themselves as journalists. Our description of science.net provides an example of an epistemic game: a simulation of professional training in game form (Shaffer, 2005, 2006). Epistemic games are computer role-playing games based on the professions. In these games, players become professionals-in-training to learn what it means to think like a professional: to ask questions, solve problems, and explain and justify answers the way people solve problems in the real world. Such games have been more thoroughly described elsewhere (Halverson, 2005a, 2005b; Shaffer, 2005, 2006; Shaffer & Gee, 2005; Shaffer, Squire, Halverson, & Gee, 2005; Squire & Jan, 2005; Zibit & Gibson, 2005). More than a decade of research, including controlled studies, has shown that epistemic games can be a productive approach to the development of games for learning. Here, we look at how and why such games might be useful as an opportunity for young people with disabilities to explore possible career paths and options. Epistemic games give players realistic images of a possible self that are constructive, motivating, and tied to the skills, knowledge, values, and ways of thinking that will prepare them for success in school and later in life. The point of such games is not to train young people in specific professions in the traditional sense of vocational education; that is, they do not train young people to be professionals. Rather, the idea is to train them to be the kind of people who can think like professionals. This makes epistemic games a potentially useful resource for special education, because exercises that provide young people with opportunities to experience professional and other work settings have been shown to increase academic achievement for students (Plank, 2001; Stone & Aliaga, 2003), and to increase graduation rates, enrollment in higher education, and earning potential (Hughes, Bailey, & Mechur, 2001; Schargel & Smink, 2001; Smink & Schargel, 2004)—areas where students with disabilities have historically faced challenges. Our goal here is not to prove that games in general— or epistemic games in particular—are good activities for students with disabilities. Rather, we aim to describe how and why a particular kind of game is useful for educational purposes as a way of starting a discussion about

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the potential of educational games in special education, especially as a means to help youth with disabilities develop realistic experiences in work- and career-related activities1.

Background Epistemic games are based on the idea that any community of practice has a particular way of helping newcomers to the community develop the practices of the group. Community of practice refers to a group of individuals with a common repertoire of knowledge about and ways of addressing similar (often shared) problems and purposes (Johnson & Bremer, 2005; Lave & Wenger, 1991). Any community has reproductive practices; that is, activities through which individuals reframe their identities, interests, and ways of thinking to fit into the community. For example, the training and apprenticeship of carpenters, lawyers, midwives, and tailors are the reproductive practices through which the next generation of carpenters, lawyers, midwives, and tailors is developed. Participation in a given community of practice involves developing that community’s ways of doing, being, caring, knowing, and thinking—what has been referred to elsewhere (Shaffer, 2004a, 2004b) as the epistemic frame of the community. Different communities of practice (i.e., different professions) have different epistemic frames. For example, lawyers act like lawyers, identify themselves as lawyers, are interested in legal issues, and know about the law. These skills, affiliations, habits, and understandings are made possible by looking at the world in a particular way—in this case, by thinking like a lawyer. If a community of practice is a group with a local culture, then the epistemic frame consists of the conventions that individuals internalize when they become acculturated, and the reproductive practices of the community are the means by which new members develop that epistemic frame. Participating in communities of practice is a useful approach for students with disabilities who are preparing for a profession. Often these students view learning as a series of different subjects in which they learn isolated, abstract pieces of information. Hence, they need additional support to understand the relevance of how their learning applies to authentic situations. Additionally, communicating and explaining their thinking as well as JSET Volume 22, Number 3

Journal of Special Education Technology skills associated with increased conceptual understanding are often particularly difficult for students with disabilities. Communities of practice provide a safe, supported environment in which students can learn and practice these skills in a real world situation. Schön (1985) argued that professional communities of practice are characterized by a particular kind of thinking and action, which he describes as reflection-in-action. Professionals literally think in action, and Schön argues that professionals develop this ability in the professional practicum. Professional practica are environments in which a learner acts as a professional in a supervised setting and then reflects on the results of his or her action with peers and mentors. Ways of knowing and ways of doing become more and more closely coupled as the novice progressively adopts the epistemic frame of the community. Examples include internship and residency for doctors, moot court for lawyers, or practice teaching for teachers. Reflective practice is developed in the progressive internalization of an epistemic frame through action in a practicum scaffolded by the knowledge, skill, and values of peers and mentors. The ways in which reflective practitioners develop their epistemic frames may provide an additional educational model that would benefit all students, including those with disabilities. Rather than constructing a curriculum based on the ways of knowing of mathematics, science, history, and language arts, we can imagine a system in which students learn to work (and thus to think) as carpenters, lawyers, architects, engineers, journalists, and other valued reflective practitioners. This would not train them for these pursuits in the traditional sense of vocational education, but developing those epistemic frames provides students with an opportunity to see the world in ways that are well aligned with the core skills, habits, and understandings of a post-industrial society and, therefore, lets them explore possible avenues for their transition from school to work within a structured environment. To use professional training as a model for educational activities, one has to analyze the structure of a professional practicum, which means understanding how activities link epistemology, practice, identity, interest, and understanding together to form the epistemic frame of


the practice. Because some parts of the practicum are more central to the creation of an epistemic frame than others, analyzing how the epistemic frame is created reveals what it might be safe to leave out. Thus, such an analysis guides the development of tools to adapt the activities that are used to train professionals in ways that are appropriate to the skills, habits, understandings, and abilities of young people. The result of such a process is a simulation that preserves the connections between knowing and doing central to an epistemic frame—a form of simulation that the first author and his colleagues refer to as an epistemic game.2 To illustrate how this process of game development works—and to explore what happens when adolescents play such games—we describe the development and testing of the epistemic game science.net, in which players become journalists writing for an online science news magazine. To explain the game, though, we first need to look at the profession—and more important, the professional practicum—on which it is based.

The Elements of Journalism While journalism schools play an important role in helping new reporters prepare for entry level work in the profession (Rhodes & Davies, 2003), there is little information on how journalism courses develop professional skills, because school is only the beginning of a journalist’s training. Journalism is by its very nature filled with writers, but surprisingly little has been written about how journalists learn their profession. A few writers have looked at the kinds of personal changes that a cub reporter goes through in becoming a professional journalist (Franklin, 1986; Gardner, Csikszentmihalyi, & Damon, 2001; Murray, 2000; Stewart, 1998), but most research on journalism education is more specialized, focusing on such things as the role of ethical theory in textbooks, how survey research can help students learn journalism, or whether Web-based tools can help teach basic writing skills (Boyle & Schmierbach, 2003; Henderon, 2002; Peck, 2003). In the genre of investigative news reporting, there are three critical things a novice reporter needs to be able to

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Journal of Special Education Technology do: (a) write to formula, (b) write as a watchdog, and (c) write for story.

A journalist—particularly an investigative journalist— writes to formula as a watchdog for the public trust.

Writing to Formula

Writing for Story

The concepts of accuracy and verification are at the core of journalism’s values, but journalism’s claim to fairness is not that reporters manage the superhuman feat of being unbiased. Rather, the methods of reporting and writing are systematic: They produce stories by a uniform set of rules. Thus, journalists write within a tightly prescribed genre, and how-to books repeatedly emphasize that news stories—and thus good reporters—follow the formula of journalistic prose (Brooks, 1989; Giles, 1969; Murray, 2000). The goal of this formula is to produce stories that appear to have no writer: stories written not in the distinctive voice of a reporter but in the generic voice of the newspaper. There is a very practical reason for this, since writing in a generic voice makes it easier for teams of reporters and copy editors to work on the same story without worrying about preserving the distinctive voice of one or another writer. But a story without a writer also appears more truthful. It is impossible to ever be completely objective, but the formula of news reporting is designed to make stories sound objective.

In order to use the journalistic formula and fulfill their role as investigators, informers, and explainers, journalists have to write about something in particular. As one introductory text explains, in the end, the job of any media writer is to “tell stories” (Bunton, Connery, Kanihan, Neuzil, & Nimmer, 1999, p. iv). Franklin (1986) argues that a news story is, ultimately, about specific people. “A story,” he writes, “consists of a sequence of actions that occur when a sympathetic character encounters a complicating situation that he confronts and solves” (p. 71). He notes that any journalistic story “must be told in terms of unique individuals and their specific actions and thoughts” (p. 75).

Writing as a Watchdog Journalists write to formula to present information that readers need as citizens in a democratic society. That is, they use the formula of journalism writing to give readers information and help them make sense of it. In interviews, journalists describe their role as “transforming data into information—by presenting objective facts so that they will have subjective meaning and, thereby, empowering the public to make adaptive choices” (Gardner, Csikszentminalyi, & Damon, 2001, p. 50). Studies of journalism emphasize how the press can make public debate possible. An important part of making public debate possible is, of course, making people aware of problems: As one overview of modern journalism described it, to “monitor power and offer voice to the voiceless” (Kovach & Rosenstiel, 2001, p. 111). Journalists write to formula to put facts on the record and help the public make sense of them, and investigative journalists focus on bringing to light facts about forces and institutions of society that might otherwise remain hidden.

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The goal of journalism is to tell readers about issues that matter in their lives. But to do so, journalists write about the stories of particular people and the things that happened to them. Franklin suggests that in journalism, “the universal is finally achieved by focusing down, tightly, even microscopically, on specific events and the details that surround them” (p. 181).

Practicum In order to help adolescents explore a career path in journalism, it was necessary to first understand how novice reporters learn to think and act as journalists. Therefore, Hatfield, Magnifico, and Shaffer (Shaffer, 2006) conducted a study of college-age students in Journalism 828 (J-828), a capstone practicum course on in-depth reporting at the University of Wisconsin. In the class, 12 advanced undergraduate and beginning graduate journalism students worked in teams of four over the course of a semester to produce investigative news reports suitable for publication in a local newspaper. The students were guided by a nationally known reporter on the faculty, with help from five local editors and reporters. During the semester, the novice reporters filed three news stories. While writing, they collaborated in three ways: through war stories, news meetings, and copy editing. Each of these forms of collaboration gave students opportunities to explore sets of values, skills, and knowledge related to journalism with each other and with more


Journal of Special Education Technology experienced journalists. At the same time, the researchers had an opportunity to examine this behavior and develop a more robust understanding of how individuals develop an identity as a journalist.

Developing an Identity as a Journalist Each of the kinds of reflection employed in the practicum was focused on a different aspect of the knowledge, skills, and values a journalist uses in reporting stories. That is, war stories emphasized writing as a watchdog. News meetings emphasized writing for story. Copy editing emphasized writing to formula. But the activities of the practicum were not only about knowledge, skills, and values. They were also about how students saw the world and saw themselves. Before looking at seeing the world, first we will focus on the issue of identity. The professor, Kate, spoke repeatedly in J-828 about what she called “good journalism: critical, skeptical, knowledgeable, smart, and—we hope—beautifully written.” But with that, she talked not just about doing smart reporting, but about being a smart reporter. She contrasted being a smart reporter with someone who is still a police reporter or a beat reporter, who covers the same formulaic stories day after day. War stories, news meetings, and copy editing in J-828 were not just about the knowledge, skills, and values of writing as a watchdog, writing for story, and writing to formula. They were about being journalists and what that means. In explaining the role of a reporter as a watchdog, for example, a visiting journalist said: “Eventually, word of scandal leaks to someone honest—they’re looking for someone to talk to. If you’re a reporter, you want to be the person they think of.” In one war story, Kate suggested that politics is “a manipulated system: You need to be the one person not manipulated.” The professional thinking that a journalist does is not just about knowledge, skills, and beliefs. It is also about seeing yourself as a journalist who knows, does, and cares in these ways. Having the opportunity to see yourself in this way may be particularly helpful to students with disabilities whose perceptions of themselves as learners are vulnerable and often inconsistent with their actual performance (Meltzer, Katzir, Miller, Reddy, & Roditi, 2004; Stone & May, 2002). The opportunity to become a member of a profession through this format can help


them develop a more realistic view of themselves as capable workers.

Epistemology The goal of the practicum was not just to help novice reporters see themselves as investigative reporters. It was also to help them learn to think like investigative reporters. For example, in an investigative news story, Kate explained, “the important question [is]: What is the story behind the story? Cops and courts are about changes in people’s lives, [but] pattern recognition will define a reporter who really goes somewhere and one who is still a police reporter.” This pattern—what Kate called “the story behind the story”—is the larger social or political problem that is the cause of some specific event. As Kate explained in an interview: “ [My goal is] to produce smart reporters ... [who] think like journalists.” Learning to think like a “smart” journalist in J-828 meant understanding that a good investigative report takes a specific set of events (the story) and represents them in the formula of journalism (a story) to shed light on problems of the larger systems that organize society (the story behind the story). By the end of the practicum, the novice reporters had internalized this way of thinking about journalism.

The Science.net Game3 If identity is central to a journalism practicum—that is, if part of learning to think like a journalist is learning to think of oneself as a journalist—what happens when young people go through a game based on a journalism practicum without actually intending to become journalists? Will they still learn to think like journalists? And what will happen to their sense of self? To find out, we conducted a series of studies with adolescents (see Hatfield & Shaffer, 2006; Shaffer, 2006) who played science.net, the computer-based game based on our investigation of the profession and training of journalists in J-828. One study was part of an after-school enrichment program at the University of Wisconsin, in which 14 middle school students (five female, nine male) from the Madison metropolitan area played for 12 hours over four consecutive Saturday afternoons. In a second study, 10 middle school students (four female, six male) 61

Journal of Special Education Technology from a program designed for at-risk youth played for 45 hours: three hours each morning for three weeks.

The Game Science.net recreates essential elements of the J-828 environment: the action and reflection that were central to the development of the skills, knowledge, identities, values, and epistemology of journalism. Science.net has been studied in some depth (Hatfield, 2003; Hatfield & Shaffer, 2006; Shaffer & Squire, 2006). Here we only present an overview of results from two studies involving adolescents and Science.net. (Interested readers may consult the aforementioned publications for more detail plus information regarding additional studies.) In science.net, players pitch stories for the health and medicine, technology, and environment sections of a magazine. Working with desk editors for each section, players interview sources, submit stories for copyediting, and copyedit each other’s work. Visiting journalists hold news meetings, where they tell war stories and talk about stories in progress. To produce finished stories, players learn to write leads and headlines, use the neutral voice of the newspaper, source their stories using AP style, include art and captions, format their work for distribution on the Web, and prioritize copy on the section front. By the end of a game, players often collectively have produced some 50 news articles about science and technology.

Adolescent Students’ Experiences In their studies with adolescents, Hatfield and Magnifico recorded players in the game using audiotape and videotape, and kept copies of all the players’ work. They also interviewed players before and after the game, asking questions about journalism and science. Summer players were interviewed again in the fall after they had returned to school. Hatfield and Magnifico asked whether and why players had decided to play the game and what they thought of the game after it was over. They also gave players a set of transfer scenarios about science and its impact on society. In both of these studies, players began to develop the skills, knowledge, identities, values, and epistemology of the profession. In the after-school outreach program, players used more journalism skills in their final stories than in their first stories. They gave more balanced in-

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formation and attributed more information to specific sources, important techniques of journalistic writing. They learned to organize their stories with leads and inverted pyramids, putting the most important information first, as in many straight news stories. Players knew and used more journalistic terms of art after the game than before the game: almost three times as many, on average. Players could use this knowledge to talk about journalism stories with considerable insight. For example, we can compare players’ copy edits of each other’s stories from early and late in the game. Commenting on another player’s draft of the first story of the game, one player said: “I liked everything, especially I liked his lead because he had everything in there, all of the 5 W’s and an H [who, what, when, where, why, and how].” This comment uses terms of art (a lead, 5 W’s and an H), but it has very little to say about the story and nothing about how to improve it. In contrast, talking about another player’s draft of the final story, the same player used journalistic terms of art to dissect the workings of the story and offer suggestions to make it better: Okay, the first [suggestion] is in the first sentence ... where she introduces the Waisman Center, but she doesn’t tell us where it is—and I don’t know where it is, and I never heard of it before. And after that it’s in the same sentence ... she starts talking about...stem cell research...but then she introduces it in the second paragraph instead. … [I]f she puts it there, people might be like: “What is that?” And then they keep reading on, and then they find out. ... She should introduce it in the lead. ... [In the second paragraph] I think you should be a lot more specific here because I didn’t know what you mean when you say disabilities—like, what kind of disabilities?

Her comments continued at the same level of detail throughout the text of the story, showing a dramatic difference in her knowledge and skill about journalism after playing science.net. The players that Hatfield and Magnifico interviewed began to develop the values of journalistic writing. When asked “What does it mean to be a journalist?” before the game, nearly four-fifths of players talked about writing without any mention of readers or writing for the public. Before the game, for example, one player said: “A journalist is someone who would write because they want to but they get paid to do it.” After the game more than three-fifths of the players talked about journalists as people who write to inform other people about important


Journal of Special Education Technology events: “To be a journalist,” said the same player after the game, “[is] to inform people about current events by writing about them.” Players came to understand the epistemology of journalism. For example, before and after the game Hatfield and Magnifico gave players a tip—some information that could potentially form the basis of a news story—and asked them: “If you were a reporter given this information, what would you do with it?” Before the game, a typical answer focused on getting more information: “I’d get more information on it, because there isn’t very much here, and I’d probably, like, ask some people what they thought.” After the game, players talked specifically about what kind of people they would speak with and why, justifying the choices in terms of the structure of a balanced news story: I would go and interview whatever scientist that discovered this. And then I would interview a few environmentalists about what’s happening. It would give me all the information of the story and would give me opposing sides of view … Because it could be biased if you just include the scientist or the environmentalist point of view … You want both sides of view to be included in the story.

Or, as one player explained, “if you support both sides and tell [the reader] what’s good about each one and what’s bad about each one, they can decide for themselves.” Finally, while developing the skills, knowledge, values, and epistemology of journalism, players came to see themselves as journalists. As one player explained at the end of the game: [The game] put ... us in a journalist’s view. So then once I was in the journalist’s view, I was like doing a journalist’s work, me being a journalist. I started to like it myself because, you know, this is what a journalist does every single day.

In other words, even though these middle school students who played science.net did not plan to be journalists when they started the game, they began to develop the skills, knowledge, identity, values, and epistemology of journalism. The Broader Impact on Players. While learning about journalism by working on science news stories as part


of a practicum, players also learned quite a lot about science. Not only did they learn scientific facts and theories related to their stories, whether about nanotechnology (“Small Technology Goes to War”), ecology (“Study: Phosphorus Threatens Mendota”), or information technology (“Can Games Really Help Children?”); what is more striking is how the game changed their understanding of what science is and why it matters. Players came to see science and scientific issues the way a journalist does: as something that matters because it has an impact on the public. For example, when asked “What is science?” before the game, one student saw science as a list of topics (the kind of topics that one studies in science class): I think science is ... things that include electricity or the human body. … I just, like, do science. … I don’t really think about what science is.”

After the game, the same student talked about science as a broad field of inquiry, touching on a wide range of issues that matter to people in the world. Before the game, players described science in terms of school subjects and topics (“electricity or the human body”) nearly eight times more often, on average, than they talked about the impact of science on society. After the game, they spoke about it more in terms of its social impact than its place in the curriculum. From preinterview to postinterview, the mean number of players’ references to science as a school subject went down from 2.21 to 1.14, and the mean number of references to science as a social force went up from 0.29 to 1.50 (p