MULTIMEDIA-ENHANCED INSTRUCTION IN ONLINE LEARNING ENVIRONMENTS
by Barbara Ann Schroeder
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Education in Curriculum and Instruction Boise State University
April, 2006
BOISE STATE UNIVERSITY GRADUATE COLLEGE SUPERVISORY COMMITTEE FINAL READING APPROVAL of a dissertation submitted by Barbara Ann Schroeder I have read this dissertation and have found it to be of satisfactory quality for a doctoral degree. In addition, I have found that its format, citations, and bibliographic style are consistent and acceptable, and its illustrative materials, including figures, tables, and charts are in place. _____________________________ Date
________________________________________ Carolyn Thorsen, Ph.D. Chair, Supervisory Committee
I have read this dissertation and have found it to be of satisfactory quality for a doctoral degree. _____________________________ Date
_______________________________________ Richard Johnson, Ph.D. Member, Supervisory Committee
I have read this dissertation and have found it to be of satisfactory quality for a doctoral degree. _____________________________ Date
_______________________________________ Lawrence Rogien, Ph.D. Member, Supervisory Committee
I have read this dissertation and have found it to be of satisfactory quality for a doctoral degree. _____________________________ Date
_______________________________________ Chareen Snelson, Ed.D. Member, Supervisory Committee
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BOISE STATE UNIVERSITY GRADUATE COLLEGE COLLEGE OF EDUCATION FINAL READING APPROVAL
To the Graduate Council of Boise State University: I have read this dissertation of Barbara Ann Schroeder in its final form and have found it to be of satisfactory quality for a doctoral degree.
Approved for the College of Education:
___________________________ Date
______________________________________ Diane Boothe, D. P. A. Dean, College of Education
Approved for the Graduate Council:
___________________________ Date
______________________________________ John R. (Jack) Pelton, Ph.D. Dean, Graduate College
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DEDICATION This dissertation is dedicated to my parents and husband, Carl Beavers, who have always believed in me. Thank you for the weekends you took the kids away, Carl, and for the encouragement you have always given me, Mom and Dad. I am forever grateful.
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ACKNOWLEDGMENTS Sincere appreciation is given to Carolyn Thorsen, the Chair of my committee, who has been my mentor and role-model during my years at Boise State. Also, thanks to my committee members, Rich Johnson, Chareen Snelson, and Larry Rogien, who took the time to read and help improve this dissertation.
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ABSTRACT
With newly developing multimedia technologies, incorporating simultaneous presentations of narration, images, and text, the possibilities for instruction are vast. Yet, how and when should educators use these technologies in the most effective ways to enhance learning? This is the driving question behind this research investigating the effectiveness of multimedia-enhanced instruction in online learning environments, one of the most rapidly expanding fields in education today. The basis for the use of multimedia is the assumption that when users interact with the various media technologies they learn more meaningfully (R. C. Clark & Mayer, 2003; R. E. Clark, 1983; Mayer, 2003). Multimedia learning principles, motivation principles, transactional distance theory, dual channel theory, computer self-efficacy, and visual/verbal learning preferences provide the theoretical bases for designing and analyzing these instructional enhancements. In this study, two different groups were examined: an experimental group (MM) which interacted with multimedia-enhanced instruction and a control group (No MM) which used a textbook for instruction. The research was conducted in an educational setting, with the researcher examining other possible variables that might affect student learning, such as learning styles in the visual/verbal range, computer self-efficacy, and experience with database software. It was the intent of the researcher to find out if a more dynamic form of multimedia instruction might improve learning outcomes when compared to a static, textbook-based format.
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Although learning outcomes were no better for the experimental than the control group, each group had statistically significant increases in test scores, which confirms Mayer's (2003) multimedia principle which states that carefully chosen words and pictures can enhance a learner’s understanding of an explanation better than words alone. Students in the "Very Low" category of computer user self-efficacy (CUSE) did not have significant gains from pre- to post-test scores. These students also had the lowest post-test score of all of the CUSE groups. These findings confirm other researchers' suggestions that a student's belief in his/her own capabilities affects performance. Also, gain scores were significantly higher for the MM Group than the No MM Group in the Above Average CUSE ranking. The more confidence a student has with computers might be a contributing factor in a student’s success with multimedia instruction. Students having no experience with database software had significant gain scores, consistent with Mayer's individual differences principle which says that multimedia design effects are stronger for low-knowledge learners. Students who rated themselves as "Very Low" on a computer self-efficacy survey had no learning gains, consistent with self-efficacy research. Moderate to strong visual learners did not experience improved test scores, raising questions of the importance of assessment alignment with instruction. Additionally, having high speed Internet access also may have had an effect upon learning in the multimedia group. Ongoing research in dynamic versus static multimedia instruction is needed to add knowledge to this rapidly growing field. As a result, the researcher continues to probe and ask the following questions: vii
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How can multimedia be most effectively used in online learning environments?
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When should it be used?
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What other variables involved in multimedia instruction might be important?
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TABLE OF CONTENTS ACKNOWLEDGMENTS ...................................................................................................... v ABSTRACT ............................................................................................................................ vi LIST OF TABLES................................................................................................................. xiv LIST OF FIGURES................................................................................................................ xv CHAPTER 1: INTRODUCTION .......................................................................................... 1 Online Learning Environments: Expanding Definitions ............................................. 2 Learning and Teaching in an Online Environment ...................................................... 3 Background of the Problem.............................................................................................. 4 Challenges in Online Teaching.................................................................................... 4 Meeting the Needs of the "Net Generation" .............................................................. 8 Theoretical Framework..................................................................................................... 9 Statement of the Problem ............................................................................................... 10 Importance of the Study ................................................................................................. 10 Assumptions..................................................................................................................... 12 Limits................................................................................................................................. 12 Delimits ............................................................................................................................. 13 CHAPTER 2: REVIEW OF THE LITERATURE............................................................... 14
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The Growth and Evolution of Online Learning Environments ................................ 14 Mayer’s Cognitive Theory of Multimedia Learning................................................... 16 Dual Channel Assumption ........................................................................................ 16 Limited Capacity Assumption .................................................................................. 17 Active Processing Assumption.................................................................................. 17 Multimedia Learning ...................................................................................................... 19 Multimedia Principle .................................................................................................. 19 Spatial Contiguity Principle....................................................................................... 19 Temporal Contiguity Principle.................................................................................. 20 Coherence Principle .................................................................................................... 20 Modality Principle....................................................................................................... 20 Redundancy Principle ................................................................................................ 20 Individual Differences Principle ............................................................................... 21 Clark and Mayer’s Additional e-Learning Principles ................................................ 21 Personalization Principle............................................................................................ 21 Interactivity Principle ................................................................................................. 21 Signaling Principle ...................................................................................................... 21 Motivation Principles...................................................................................................... 24 Dual Coding Theory........................................................................................................ 26 Moore’s Transactional Distance Theory....................................................................... 29 Criticism of Moore's TDT ........................................................................................... 31 Computer Self-Efficacy ................................................................................................... 32
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Learning Styles................................................................................................................. 33 Review of Learning Styles Theories.............................................................................. 35 Instructional Interactivity ............................................................................................... 38 Four Essential Elements of Instructional Interactivity........................................... 39 Epistemological Underpinnings of Learning with Technology................................ 40 Additional Research on the Effects of Multimedia in Online Learning .................. 42 CHAPTER 3: METHODS AND PROCEDURES.............................................................. 45 Research Questions and Hypotheses Statements ....................................................... 45 Research Design............................................................................................................... 46 Participants ....................................................................................................................... 46 Treatment.......................................................................................................................... 49 Instruments....................................................................................................................... 50 Pre- and Post-Tests...................................................................................................... 50 Computer User Self-Efficacy Survey ........................................................................ 52 Learning Styles Survey............................................................................................... 53 Data Collection................................................................................................................. 55 Data Analyses................................................................................................................... 56 CHAPTER 4: RESULTS....................................................................................................... 58 Introduction...................................................................................................................... 58 Distribution of Data......................................................................................................... 58
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Independence of Samples............................................................................................... 61 Learning Outcomes between Groups ........................................................................... 61 Learning Outcomes within Groups .............................................................................. 62 Computer User Self-Efficacy (CUSE) Analyses........................................................... 62 Gain Scores across CUSE Groups ............................................................................. 63 Visual and Verbal Learning Styles Analyses............................................................... 69 Experience with Microsoft Access ................................................................................... 72 Interaction Effects of CUSE Rankings, ILS Groups, Experience with Database Software, and Instructional Groups on Gain Scores............................................... 75 Correlations between CUSE Scores, Visual/Verbal Learning Preferences, Experience with Database Software, and Pre- and Post-Test Scores.................... 76 Predicting Post-Test Scores using Regression Analyses ............................................ 76 High Speed Internet Access and Post-Test Scores in the MM Group ...................... 78 CHAPTER 5: CONCLUSIONS........................................................................................... 80 Revisiting the Original Research Questions ................................................................ 80 Conclusions ...................................................................................................................... 80 Question One ............................................................................................................... 81 Question Two............................................................................................................... 83 Recommendations for Future Research ....................................................................... 85 REFERENCES....................................................................................................................... 90
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APPENDIX A...................................................................................................................... 100 Computer User Self-Efficacy (CUSE) Survey ............................................................ 100 APPENDIX B ...................................................................................................................... 105 Index of Learning Styles (ILS) Survey ........................................................................ 105 GLOSSARY ......................................................................................................................... 111
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LIST OF TABLES Table 1
Clark and Mayer’s Eight Multimedia Principles (2003)................................. 23
Table 2
Tests of Normality by Instructional Groups.................................................... 59
Table 3 Means and Standard Deviations of No MM and MM Groups ..................... 60 Table 4
Dependent Samples t-test on CUSE Groups ................................................... 64
Table 5 Wilcoxon Signed Ranks Test of CUSE Groups and Gain Scores.................. 65 Table 6 Post-Test and Pre-Test Scores Arranged by CUSE Rankings ....................... 66 Table 7 Pre- and Post-Test Scores Categorized by Type of Instruction and CUSE Groups ................................................................................................................... 67 Table 8 Mean Ranks of Visual/Verbal Preferences Compared to Post-Test Scores 72 Table 9
Test Scores Categorized by Experience with Microsoft Access.................... 73
Table 10 Mean Ranks of Experience with Microsoft Access to Post-Test Scores ....... 74 Table 11 Experience with Microsoft Access across Gain Scores................................... 74 Table 12 Regression Statistics Details............................................................................... 77 Table 13 High Speed Internet Users Mean Scores by Instructional Groups............... 78
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LIST OF FIGURES Figure 1. Cognitive theory of multimedia learning. ....................................................... 18 Figure 2. General model of Dual Coding Theory (DCT)............................................... 28 Figure 3. Essential elements of instructional interactivity. ............................................ 39 Figure 4. Age range distribution of participants (N=60)................................................ 48 Figure 5. CUSE groups and gain scores by instructional groups.................................. 69 Figure 6. Percentages of learning styles in Visual/Verbal continuum for No MM group.............................................................................................................................. 70 Figure 7. Percentages of learning styles for MM group. ................................................ 71
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CHAPTER 1: INTRODUCTION This is an exciting, yet challenging time for online instruction. The increasing availability of high-speed Internet, faster and more powerful personal computers, and wireless Internet hot spots provide learners with more opportunities to access, view, and participate in an online learning environment. More web-based applications are being developed and deployed to meet the expanding demands of the mobile learner. In the process, we are seeing student-teacher roles being transformed, as students shoulder more of the responsibility for learning and teachers assume roles of mentors and guides. "The locus of ownership of both the process of constructing and sharing knowledge, and of knowledge itself, is shifting. Learners are not only willing to participate in the construction of knowledge; they are starting to expect to" (The Horizon Report, 2005 edition, 2005, p. 4). Yet, many challenges surface from this new evolution of teaching, learning, and technologies. As instructors strive to create more meaningful, useful, and engaging online content they are faced with choosing the appropriate software, learning how to use it, and most importantly, using it in the most effective ways for learning. Therefore, this research was undertaken to evaluate the effectiveness of multimedia-enhanced instruction in an online learning environment. Knowing how and when to use multimedia can be guided by Mayer's (2003) multimedia principles. However, it is also important to understand that the medium alone is simply a way of delivering instruction. Well-conceived and implemented
2 instructional strategies should form the underlying structure of the medium. “Effective instruction, independent of particular media, is based upon the selection and organization of instructional strategies, and not simply the medium per se” (Hannfin & Hooper, 1993, p. 192). The critical features of effective instructional media are pedagogical, not technical (R. E. Clark, 1983). Therefore, in evaluating the effectiveness of multimedia instruction, it must be understood that the instruction must include and demonstrate research-based multimedia learning principles. Learning is complex, with students responding emotionally, imaginatively, and socially to instruction (Eisner, 2005). A cognitive approach emphasizes learning as an interconnected process, with the student actively involved in mediating learning. Therefore, besides evaluating the effectiveness of learning in a multimedia environment, other factors are addressed, such as differences in student learning styles, attitudes towards computers, and background knowledge. In evaluating these additional factors, it is anticipated that a richer and more complete picture of the effectiveness of multimedia-enhanced learning will be revealed.
Online Learning Environments: Expanding Definitions There are many words that are used to define online learning environments, such as distance education, e-learning, web-based instruction, online learning, extended learning, the use of course management systems, such as Blackboard or WebCT, and hybrid or blended learning, which integrates both face-to-face and an online component. The line of demarcation between traditional face-to-face learning and online learning is becoming more blurred, with many face-to-face courses being augmented and enriched
3 by online components, such as asynchronous and synchronous online discussions, the posting of assignments, materials, and grades online, online submission of assignments, and virtual meeting spaces for student collaboration. At Arizona State, for instance 11,000 students take fully online courses and 40,000 use the online course management system. At Boise State University, the percentage of students enrolled in Blackboard, a course management system, further illustrates the spread of online learning in traditional, face-to-face courses, with 72% of the total school population enrolled as of fall, 2002 (Academic Affairs Annual Report, 2002). Therefore, the definition of “online learning” or “online learning environment” in this study shall be expanded to include any instruction that uses technology to deliver some or all of a course that can be accessed via a web browser.
Learning and Teaching in an Online Environment The Internet and WWW can be described as hypertext learning environments, where students can work when and how they wish, access rich, comprehensive resources for research and discussion, and communicate with their instructor and classmates in multiple, nonlinear ways. Marchionini (1988) described the hypertext learning environment as a self-directed, information-fluid environment with high teacher-learner interaction. Online learning environments can be unplanned and discovered as well as learner-activated, self-motivated, self-directed, non-sequential, dynamic, and recursive. The Internet can offer a unique learning space that is exciting
4 and powerful, with learners determining how, when, and what is to be learned (Wang & Bagaka's, 2003). Although there remains disagreement on whether or not this medium of learning is as effective as or better than the traditional, face-to-face method of learning (Bachman, 1995; Collins, Hemmeter, Schuster, & Stevens, 1996; Denman, 1995; Ellery, Estes, & Forbus, 1998; Rintala, 1998; Russell, 1999), a student’s experience with online instruction is different in key ways. Online instruction requires the teacher to view and understand learning from new paradigms, to teach from different perspectives, and to use evolving teaching strategies and technologies to effectively help students learn. In fact, it has been suggested that the traditional model of systematic instructional design may no longer be appropriate for teaching with these new technologies (Gillespie, 1998; Pelz, 2004).
Background of the Problem Challenges in Online Teaching Since the emergence of the Internet and the World Wide Web (WWW) in providing instruction in the mid-1990s, there have been numerous studies about the problems of designing web-based instruction. Most of these studies have had “common shortcomings” in that they have failed to develop a theoretical or conceptual framework of web-based, or online instruction (Jung, 2001, p. 526). Indeed, the process of designing online instruction can be so complex and difficult that educators often end up “adopting curriculum to fit the technology rather than selecting the proper technology to support the curriculum” (Bennett & Green, 2001, p. 1).
5 According to Green’s 2004 Campus Computing Survey (2004), assisting faculty efforts to integrate technology into instruction has remained a challenge in higher education. Also, there is conflicting research on what constitutes effective online learning experiences (Dillon & Gabbard, 1998; Ellery, Estes, & Forbus, 1998; Frear & Hirschbuhl, 1999; Honey, 2001; Laurillard, 2003; Quitadamo & Brown, 2001). Many educators now believe that the unique environment of online learning necessitates a reexamination of the learning process, in many instances a paradigm shift in pedagogical practice (Bennett & Green, 2001; Gillespie, 1998; Idrus & Lateh, 2000; Jung, 2001; Laurillard, 2003). For instance, changing a traditional face-to-face course to an online course does not mean posting lectures online in a text-based format. Rather, it involves a transformation of both teaching and learning, a process that requires training and possibly a change in an instructor’s style and expectations. Time is another challenge. Faculty must work with time constraints and communicate and follow-through with email, grading, discussion boards, and online chats. They must be able to support and nurture a community of learners, motivate and inspire, gain their attention, and get them to learn. At the same time, faculty must also be cognizant of available and evolving technologies and how to use them to effectively support and enhance student learning. As a result, educators need to constantly reflect upon, improve, and update their practice, understanding how to best design instruction to support student learning. These can be difficult, if not impossible, goals, given the time that most instructors of higher education must spend on teaching, research, and service (Turley, 2005).
6 Faculty may also need to learn new skills to create and implement rich online learning experiences. Those who want to augment their instruction with online components need to learn how to use those tools, such as synchronous meetings, tutorials, simulations, multimedia lessons, instant messaging, blogs, wikis, RSS, the use of course management systems, and other interactive multimedia formats. Additionally, instructors need to understand human learning processes. As Clark and Mayer (2003) tell us, when the limits of human cognitive processes are ignored, instruction that employs all of the technological capabilities to deliver text, audio, and video can actually reduce or hinder learning. An understanding of educational psychology, instructional design, multimedia production, graphics, and interface design are necessary to translate these principles into effective online instruction. Although new technologies ease the burden of knowing a programming language, it still takes from ten to twenty times more labor and skill to produce good courseware for online learning than for traditional classrooms (Clark & Mayer, 2003). Another challenge of online learning environments is the shortage of technical staff to help faculty, students, and staff. This shortage can put a strain on developing web-based programs and delay worthwhile projects. Many issues continue to confront institutions of higher education in the realm of online learning. In the October, 1997, report on “Distance Education in Higher Education” (Lewis, Alexander, & Westat, 1997), higher education institutions were reported as having the following goals for development of distance learning programs: •
Reducing per-student costs
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Making educational opportunities more affordable for students
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Increasing institution enrollments
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Increasing student access by reducing time constraints for course taking
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Increasing student access by making courses available at convenient locations
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Increasing institutional access to new audiences
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Improving the quality of course offerings
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Meeting the needs of local employers
The good news is that instructors now have access to rich multimedia tools to enhance instruction. The bad news is that multimedia software is often used in instructionally-deficient ways. For instance, PowerPoint is multimedia software that is easy to use, but can be detrimental to learning if used in the wrong ways. It is still common to see instructors read textual bullets from a PowerPoint, a method that violates Mayer's multimedia and redundancy principles. Faculty in higher education may need to receive training on how to effectively integrate multimedia in instruction. This is indicated by the availability of training courses offered by various universities. An example of one course offered by the Illinois Online Network, "Multimedia Principles for Online Educators," available at http://www.ion.uillinois.edu/courses/catalog/C-CourseDetail.asp?course=11 provides instruction of how to effectively design multimedia instruction. As Richard Felder, designer of the Index of Learning Styles (ILS) survey, bluntly writes:
8 College teaching may be the only skilled profession for which no preparation or training is provided or required. You get a Ph.D., join a faculty, they show you your office, and then tell you, “By the way, you're teaching 205 next semester. See you later.” (Felder, 2006, p. 1).
Meeting the Needs of the "Net Generation" We are experiencing and educating a new generation of learners, sometimes called the "Net Generation," students who have grown up with technology and computers. These students have different skills and needs in the realm of instructional technology and bring a new set of expectations to the classroom. For instance, here are some informal comments from Net Generation students in response to the open ended questions, "To me, technology is . . . " (Roberts, 2005)
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"Reformatting my computer system and installing cutting-edge software that allows me to do what I want, when I want, without restrictions, viruses, and the rules of Bill Gates." —Jody Butler, Junior, Idaho State University
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"The ability to adapt and configure an already established program to [something that] benefits me daily, be it customizing WeatherBug to state the weather in my particular region or formatting my cell phone pad to recognize commonly used phrases in text messaging." —Christopher Bourges, Senior, Duke University
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"Any software and hardware alike that gives me the power to do what I need to do faster than ancient methods of conducting things, such as e-mailing versus writing, messaging three people versus buying a three-way calling package,
9 digital research versus traveling to a well-stocked library, et cetera." —Lindsey Alexovich, Senior, American University
In these short narratives, one can clearly see the importance of staying in tune with one's students and their technology expectations. Instructors, therefore, need to keep abreast of new technologies and how students use them. They need to design instruction that is relevant and engaging, knowing that students have high expectations for content, accessibility, and easy of use.
Theoretical Framework The theoretical framework of this research will be based on understanding various theories that support multimedia learning, aspects and theories of learning online and a brief overview of the epistemological underpinnings of learning in an online environment by discussing the following: •
Mayer’s cognitive theory of multimedia learning (R. C. Clark & Mayer, 2003; Mayer, 2003)
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motivation principles (Keller & Burkman, 1993)
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dual coding theory (Paivio, 1986; Sadoski & Paivio, 2001);
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Moore’s theory of transactional distance (Moore, 1993);
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Visual/verbal learning styles (Felder & Silverman, 1988);
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computer self-efficacy (Cassidy & Eachus, 2002);
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instructional interactivity (M. W. Allen, 2003); and
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epistemological underpinnings of learning with technology.
A detailed discussion of this theoretical framework is included in Chapter Two.
10 Statement of the Problem As stated earlier, there are many challenges in integrating effective multimedia instruction. Besides the advanced technology skills that instructors must possess, they must also be able to research, evaluate, and choose the software, learn how to use it, and then design effective instruction. Instructors may not have the expertise or time to effectively design web-based course materials (Kekkonen-Moneta & Moneta, 2002; Okamoto, Cristea, & Kayama, 2001; Oliver, MacBean, Conole, & Harvey, 2002). Also, evaluating the effectiveness of multimedia instruction can be complicated and prone to multiple interpretations (Ellis & Cohen, 2001; Laurillard, 1998). Clark also suggests that little research exists proving the effectiveness of one instructional medium over another (1983). Therefore, this research is undertaken to tackle the problem of knowing how and when to use multimedia-enhanced instruction in online learning environments. It draws attention to the importance of adhering to strict principles of multimedia design, while also taking into consideration other elements of learning. Also, this study was conducted to address the limited research of the effects of multimedia as observed in an educational setting.
Importance of the Study This study is an important contribution to the research of and understanding how to use web-based multimedia instruction as a learning tool. Colleges and universities are using the Internet and WWW more and more to deliver instruction, and instructors and courseware designers need to have valid information on what
11 technologies are available and how to use them to improve student learning. Students of the "Net Generation" expect and demand high quality, fully accessible course materials available online. Additionally, Macromedia Breeze, software that allows synchronous meetings and high quality asynchronous productions suitable for online presentation, has recently been purchased by the Department of Educational Technology at Boise State University and has been in use for the past year. This software not only allows instructors to provide instructional content available 24/7, but also to transform the teaching-learning environment to encourage more interaction , to narrow the transactional distance often found in an online learning environment, and to create new pedagogical models from which to teach and learn. For instance, online study groups can be formed, with students using a virtual "room" to meet and collaborate, brainstorm, or present their work. These meetings can be conducted with web cameras and microphones, enabling a seamless, virtual environment for learning and sharing. Decisions to purchase multimedia software by university departments can be justified through this research. Software companies would gain feedback about the usefulness of their products in an educational setting. Finally, addressing and comparing the effects of additional factors involved in the research outcomes, such as individual learning styles, computer self-efficacy, background knowledge, gender, and age will provide a more expansive interpretation of the study.
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Assumptions This research will be based on the following assumptions: •
Students will answer surveys honestly.
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Randomization of the student sample will meet the assumption of independence of samples.
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Databases are the most difficult part of the course content and are most suitable for a multimedia-enhanced lesson.
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Students learn differently.
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Students have different attitudes toward their learning abilities using computers (computer self-efficacy).
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Students have different background knowledge of database software.
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Students who are instructed to do so will view and interact with the required multimedia lessons.
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Students will complete their instruction.
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Cognitive learning theory is a valid theory of how people learn.
Limits The following will limit generalizability of the research: •
Student sample (60) is limited to students in four sections of an introductory educational technology class at Boise State University.
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Index of Learning Styles (ILS) survey not identified as an appropriate measuring instrument until the spring 2006 semester; therefore only 34 responses were collected on this variable.
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Affective surveys were self-reported.
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Test instruments are not intended for general use outside Boise State University.
Delimits The following will delimit the research: •
The student sample is purposive and convenient.
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Database skills, one module of the course, will be evaluated.
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The researcher will be the sole instructor.
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CHAPTER 2: REVIEW OF THE LITERATURE
The Growth and Evolution of Online Learning Environments Online learning environments in various formats are rapidly growing in institutions of higher education. Enrollment in online learning is predicted to continue to increase. In fact, online enrollment is growing faster than student enrollment. In a Sloan Consortium survey, 53.6 percent of institutions agreed that online education is critical to their long-term strategy (I. E. Allen & Seaman, 2004). And a majority of academic leaders stated their belief that the quality of online instruction is equal to or better than the quality of traditional instruction (Oblinger & Oblinger, 2005). The majority of institutions offer some type of online learning today. Three-fifths (62.5 percent) of the colleges and universities that participated in Green’s 2002 Campus Computing Survey offer at least one complete online or web-based college course (2003). An online directory of distance learning (http://www.petersons.com/distancelearning/) identifies about 1,100 institutions that provide online degree programs. Some of these include Azusa Pacific University (evangelical Christian), Boston University (large private), Cardean University (online, for-profit), De Anza College (two-year public), DeVry University (multi-campus, forprofit), Michigan State University (large public), Boise State University (medium public), and eArmyU (U.S. military). Universities that offer degrees entirely online are rapidly expanding and marketed to working professionals and other nontraditional students. The University of
15 Phoenix (http://degrees.uofphx.info/), for instance, serves approximately 45,000 adult learners in its online degree program, placing Phoenix Online among one of the ten largest colleges or universities in the United States. According to Business Week Online, the corporate e-learning market was projected to be $11.4 billion by 2003 (Schneider, 2000). As the e-learning market gains momentum and increasing visibility, some universities are also adapting it for the business sector, by spinning off their online coursework into separate, for profit ventures, such as Duke University's J.B. Fuqua School of Business. This rapid growth is due to many factors, such as the increasing sophistication and accessibility of the Internet, the changing demographics of university students, the decreasing costs of computing, and the need for people to have flexible options for learning (Cooper, 2001; Pasquinelli, 1998). An online learning environment is one way to provide a medium of instruction that enables faculty to extend teaching and learning activities. However, as stated earlier, the concept of online learning is expanding to include any form of learning that is done via a web browser. For instance, an online learning environment can be totally online, where students are not required to come to a physical classroom. It can be hybrid instruction, where students spend part of their time in the classroom and the other part learning online. It can also be an element of traditional, face-to-face classrooms, where instruction is augmented by online components. In the following sections, the theoretical framework, a natural extension of the literature review, is discussed, ensuring that the search for concepts central to the problem under investigating are understood and known research is applied. This
16 investigation also provides frameworks within which concepts and variables acquire their own significance and will help in interpreting the larger meaning of the findings.
Mayer’s Cognitive Theory of Multimedia Learning Mayer is well-known and respected for his research in the field of cognitive theory as it relates to multimedia learning. His seminal work, Multimedia Learning (2003), is rich with research on how people learn through various multimedia instructional messages. According to Mayer, a multimedia instructional message is a presentation “involving words (such as spoken or written text) and pictures (such as animation, video, illustrations, and photographs) in which the goal is to promote learning” (2002, p. 56). The driving question in his research at the University of California, Santa Barbara, has been to understand how multimedia instructional messages should be designed so that learners can demonstrate deep, conceptual understanding. Mayer links cognitive learning theory to multimedia design issues, validating three theory-based assumptions about how people learn from words and pictures: the (1) dual channel assumption, the (2) limited capacity assumption, and the (3) active processing assumption.
Dual Channel Assumption The dual channel assumption is based upon the theory that human cognition consists of two distinct channels for representing and handling knowledge: a visualpictorial channel and an auditory-verbal channel. This theory says that pictures enter through the eyes and are processed as pictorial representations in the visual-pictorial channel. The other channel consists of the auditory-verbal channel or verbal
17 representations, which includes the process of spoken words entering the cognitive structure through the ears.
Limited Capacity Assumption Limited capacity assumption is exemplified by auditory-verbal overload, when too many visual materials are presented at one time. Each channel in the human cognitive system has a limited capacity for holding and manipulating knowledge (Baddeley, 1999a, 1999b), so when a lot of spoken words and other sounds are presented at the same time, the auditory-visual channel can become overloaded.
Active Processing Assumption The third of Mayer’s assumptions, active processing, implies that “meaningful learning occurs when learners engage in active processing within the channels, including selecting relevant words and pictures, organizing them into coherent pictorial and verbal models, and integrating them with each other and appropriate prior knowledge” (2002, p. 60). Important to this assumption is the fact that these “active verbal processes are more likely to occur when corresponding verbal and pictorial representations are in working memory at the same time” (2002, p. 60). All of these assumptions are important points to consider in designing and delivery multimediaenhanced online instruction.
18 Mayer further explains, Words enter the cognitive system through the ears (if the words are spoken), and pictures enter though the eyes. In the cognitive process of selecting words, the learner pays attention to some of the words, yielding the construction of some word sounds in working memory. In the cognitive process of selecting images, the learner pays attention to some aspects of the pictures, yielding the construction of some images in working memory. In the cognitive process of organizing words, the learner mentally arranges the selected words into a coherent mental representation in working memory that we call a verbal model. In the cognitive process of organizing images, the learner mentally arranges the selected images into a coherent mental representation in working memory that we call a pictorial model. In the cognitive process of integrating, the learner mentally connects the verbal and pictorial models, as well as appropriate prior knowledge from long-term memory. (2002, pp. 60-61) Furthermore, this model is activated through five steps: (a) selecting relevant words for processing in verbal working memory, (b) selecting relevant images for processing in visual working memory, (c) organizing selected words into a verbal mental model, (d) organizing selected images into a visual mental model, and (e) integrating verbal and visual representations as well as prior knowledge (Mayer, 2003). Figure 1 is a graphical illustration of the steps in this theory.
Figure 1. Cognitive theory of multimedia learning.
Adapted from Mayer (2003).
19 Multimedia Learning Mayer’s research has resulted in the discovery of eight principles of multimedia design, each based on cognitive theory and supported by the findings of empirical research. These eight principles are explained as follows in more detail, along with their application and use in this study:
Multimedia Principle Carefully chosen words and pictures can enhance a learner’s understanding of an explanation better than words alone. Mayer tells us that students mentally connect pictorial and verbal representations of the explanation, deeper understanding can occur. In three studies where students viewed a narrated animation about pumps or brakes or simply listened to a narration, the students who viewed the narrated animation scored substantially higher (R. C. Clark & Mayer, 2003). Mayer corroborates his finding with Rieber’s (1990) finding that students learn better from computer-based science lessons when animated graphics are also included.
Spatial Contiguity Principle Mayer’s spatial contiguity principle examines how words and pictures should be coordinated in multimedia presentations. This principle states that the narration should be simultaneous with the animation. Also, words and associative pictures should be near each other. Mayer confirms his research with Baggett and others, showing that students learn an assembly procedure better when corresponding narration and video are presented simultaneously (Baggett, 1984, 1989; Baggett & Ehrenfeucht, 1983).
20 Temporal Contiguity Principle This principle states that students learn better when corresponding words and pictures are presented at the same time, rather than in succession. In other words, the narration and animation should be presented in close coordination, so that when the narration describes a particular process or action, the animation shows it at the same time. This is described as simultaneous presentation, because the words and pictures are contiguous in time or reflect temporal contiguity.
Coherence Principle This principle states that students learn better from multimedia presentations in which extraneous words, sounds, and video are excluded. Related research on this principle was presented by Kozmo (1991).
Modality Principle This principle states that students learn more deeply from animation and narration than from animation and on-screen text (a common presentation method in online PowerPoint presentations). In other words, students learn more deeply from animation and narration than from animation and on-screen text.
Redundancy Principle This principle states that students learn better from multimedia presentations consisting of animation and narration than from animation, narration, and on-screen text.
21 Individual Differences Principle This principle says that multimedia design effects are stronger for lowknowledge learners and for high-spatial learners. In other words, since high-knowledge learners already have some background knowledge, they might not need the additional instruction offered by multimedia learning. Also, high-spatial learners are more likely able to integrate the visual and verbal representations afforded by multimedia presentation.
Clark and Mayer’s Additional e-Learning Principles The following additional multimedia principles are discussed in Clark and Mayer (2003):
Personalization Principle Students learn better when words are presented in a conversational style than in an expository style.
Interactivity Principle Students learn better when they can control the presentation rate of multimedia explanations than when they cannot.
Signaling Principle It is important to incorporate signals into the narration to help the learner determine the important ideas or concepts and how they are organized. Signaling does not add any new words to the passage, but rather emphasizes key words through
22 introductory outlines, headings spoken in a deeper voice and keyed to the presentation, pointer words, and highlighted words spoken in a louder voice. Signaling can help guide the process of making sense of the presentation by directing the learner’s attention to key events and relationships. Mayer tells us that additional research is needed in this area, with prior research focused mainly on signaling of printed text (Lorch, 1989). An underlying understanding of these principles involves individual differences. Researchers have found that high-ability learners are able to process more sensory information than low-ability learners and that low-ability learners take longer and require more highly structured information (Cronback & Snow, 1977). Mayer’s multimedia learning theory offers an indispensable theoretical framework by providing clear information on how to design effective multimedia instruction. Clark and Mayer (2003) have collaborated to condense these principles of multimedia learning, which are more practitioner-based and applicable for this study. Therefore, for this study, Clark and Mayer’s eight multimedia principles form the basis for the design of the multimedia instruction. Table 1 includes each of these principles and their applications.
23 Table 1 Clark and Mayer’s Eight Multimedia Principles (2003)
Principle Multimedia Principle
Contiguity Principle
Coherence Principle
Modality Principle
Redundancy Principle
Personalization Principle Interactivity Principle Signaling Principle
Definition Students learn better from words and pictures than from words alone. Text or auditory alone are less effective than when the text or narration is augmented with visual images. Students learn better when corresponding printed words and graphics are placed close to one another on the screen or when spoken words and graphics are presented at the same time. Students learn better when extraneous words, pictures, and sounds are excluded rather than included. Multimedia presentations should focus on clear and concise presentations. Presentations that add extraneous information hamper student learning. Students learn better from animation and narration than from animation and on-screen text. Multimedia presentations involving both words and pictures should be created using auditory or spoken words, rather than written text to accompany the pictures. Students learn better from animation and narration than from animation, narration, and on-screen text. Multimedia presentations involving both words and pictures should present text either in written form, or in auditory form, but not in both. Students learn better when words are presented in conversational style than in expository style. Students learn better when they can control the presentation rate of multimedia explanations. Students learn better when signals are incorporated into the narration to highlight important ideas or concepts and how they are organized. Signaling emphasizes key words through introductory outlines, headings spoken in a deeper voice, pointer words, and highlighted words spoken in a louder voice.
24
Motivation Principles Another important factor involved in the process of designing excellent instructional messages is the extent of motivational appeal. For the learner, “motivation is an initial determining factor that colors all that follows in a learning event” (Keller & Burkman, 1993, p. 3). In fact, motivation is so important that Keller and Burkman insist that the “design of an instructional message is not complete without considering its motivational appeal” (p. 3). Therefore, for this study, principles of motivation will be considered throughout the design and development of the multimedia lessons. A brief discussion of the motivation principles appropriate for this study follows. Many of the motivational principles of Keller and Burkman (1993) focus on (1) gaining and maintaining attention, (2) relating the content of materials to learner interests, goals, or past, and (3) building and maintaining learner confidence in ability to use the materials. The following motivational directives will also be used to guide the design of the multimedia lessons: 1. Introduce problem-solving topics to stimulate an attitude of inquiry. 2. Use humor to stimulate curiosity. 3. Use explicit statements about how the instruction builds on the learner’s existing skills or knowledge. 4. Use analogies or metaphors to connect the present material to processes, concepts, and/or skills already known by or familiar to the learner. 5. The motivation to learn is greater when there is a clear relationship between the instructional objectives and the student’s learning goals.
25 6. Use personal language to stimulate human interest on the part of the learner. 7. Improve relevance by adapting your teaching style to the learning style of the students. 8. Design the challenge level to produce an appropriate expectancy for success. 9. Give learners information on what they will learn ahead of time, so they know where they will be going. 10. Build confidence and persistence by using easy to difficult sequencing of content, exercises, and exams, especially for less able and low-confidence students. 11. Provide criteria for success and answers to exercises to encourage students to use self-evaluation of performance (performance-based assessment). 12. Include learner options to promote an internal sense of control on the part of the learner. 13. Allow learners to go at their own pace to increase motivation and performance. 14. Promote feelings of accomplishment by including, in the instructional materials, exercises or problems that require the application of the new knowledge or skill to solve. 15. Use the active voice to maintain learner attention. 16. Use a natural word order to maintain learner attention. 17. Include graphics that make courseware easier to interpret and use in order to maintain learner attention and to build confidence. 18. Use interesting pictures to gain and maintain learner attention in instructional text. 19. Include pictures that include novelty and drama to maintain learner attention.
26 20. Include pictures that include people to gain and maintain learner attention. (Keller & Burkman, 1993, pp. 31-49)
Dual Coding Theory Dual coding theory (Paivio, 1986) proposes that information is stored in longterm memory as both verbal propositions and mental images. This theory is aligned with Mayer’s multimedia learning theory, stating that when information is presented verbally and visually, it has a better chance of being remembered. Corroborating research shows that concrete words are remembered better than abstract words, and that pictures alone are remembered better than words alone (Fleming & Levie, 1993). Paivio states, "Human cognition is unique in that it has become specialized for dealing simultaneously with language and with nonverbal objects and events. Moreover, the language system is peculiar in that it deals directly with linguistic input and output (in the form of speech or writing) while at the same time serving a symbolic function with respect to nonverbal objects, events, and behaviors. Any representational theory must accommodate this dual functionality" (1986, p 53). Paivio used the word “coding” to refer to the coding mechanisms humans use to process textual and visual components. Although these coding mechanisms are separate, they are also sometimes complementary. Dual coding theory (DCT) says that text uses a linguistic coding mechanism, encoding information in serial form, while graphics uses an imagery system, encoding information in a spatial format. Dual coding theory can be visualized as a framework of two cognitive subsystems, one being composed of verbal stimuli and the other, nonverbal stimuli. As
27 stated above, these two connections are not distinct, but are connected. Paivio defines two different types of representational units: "imagens" for mental images and "logogens" for verbal entities. Furthermore, DCT identifies three types of processing: (1) representational, the direct activation of verbal or non-verbal representations; (2) referential, the activation of the verbal system by the nonverbal system or vice-versa; and (3) associative processing, the activation of representations within the same verbal or nonverbal system. A given task may require any or all of the three kinds of processing. A general model of DCT is illustrated in Figure 2, which shows the verbal and nonverbal systems including representational units and their referential (between systems) and associative (within systems) interconnections.
28
Figure 2. General model of Dual Coding Theory (DCT).
© 1994-2004 Greg Kearsley (
[email protected]) http://home.sprynet.com/~gkearsley Permission is granted to use these materials for any educational, scholarly, or noncommercial purpose.
As previously discussed, Mayer’s (2003) multimedia learning theory is based on the assumptions that humans possess separate systems for processing pictorial and verbal material (dual channel assumption), each channel is limited in the amount of material that can be processed at one time (limited-capacity assumption), and meaningful learning involves cognitive processing including building connections between pictorial and verbal representations (active-processing assumption). Paivio’s (1986) dual coding theory supports Mayer’s multimedia learning theory (2003) and
29 helps explain the concept of cognitive overload, in which the learner’s intended cognitive processing exceeds his/her available cognitive capacity. A similar view of dual coding theory is called dual-processing theory by Moreno and Mayer (1999). This theory supports multimedia learning and includes two types of processing: visual and auditory. Moreno and Mayer tell us that visually-presented information is represented initially in visual working memory and then translated into sounds in auditory working memory, while auditorily-presented information is represented and processed entirely in auditory memory. Therefore, in interacting with multimedia instruction consisting of images and narration, learners represent the images in visual working memory and the corresponding narration in auditory working memory, thus avoiding the possibility of cognitive overload that could be caused by reading and processing text from visual to auditory working memory. Because students can hold corresponding visual and verbal representations in working memory at the same time, they are able to build referential connections between them. Therefore, it would seem prudent to design multimedia instruction with minimal textual input and more narration with corresponding images.
Moore’s Transactional Distance Theory Moore’s (1993) transactional distance theory (TDT) can be a useful theory from which to frame this research. This theory describes pedagogical relationships existing in an online learning environment as “the family of instructional methods in which the teaching behaviors are executed apart from the learning behaviors, including those that in contiguous teaching would be performed in the learner’s presence, so that
30 communication between the teacher and the learner must be facilitated by print, electronic, mechanical, or other devices” (Moore, 1972, p. 76). TDT first appeared in 1972 and has been reworded as changes in instruction have occurred, specifically as delivery of instruction online has increased. Researchers have tested this theory since then across different technologies, such as videoconferencing, interactive television, and computer networks (Bischoff, Bisconer, Kooker, & Woods, 1996; Chen & Willits, 1999; Gayol, 1995; Saba & Shearer, 1994) According to Moore, there are three key elements that define every online learning environment: 1. dialogue; 2. structure; and 3. learner autonomy. Dialogue refers to the extent to which teachers and learners interact with each other, structure refers to the responsiveness of instruction to a learner’s needs, and learner autonomy corresponds to the extent to which learners make decisions regarding their own learning and construct their own knowledge (Moore & Kearsley, 1996). The degree of transactional distance between the teacher and learner is related to the amount of dialogue, course structure, and learner autonomy. In other words, transactional distance would be greatest when the teacher had no interaction at all with the student and the learning materials are pre-designed and unresponsive. In an online course, an instructor would need to interact regularly with the student, be responsive and supply materials as needed to enhance the instruction, and respect the student’s autonomy in order to minimize transactional distance. Another way of looking at TDT is
31 that transactional distance decreases when dialogue increases and structure decreases, and when structure increases transactional distance also increases, but dialogue decreases. In 2003, Laurillard expanded Moore’s ideas by rating how and to what extent different types of media could be used by instructors to provide high quality learnerinstructor and learner-content interactions. Content alone presented in certain forms and by particular types of media could become a virtual teacher, Laurillard suggested. The media that received the highest ratings for teaching were tutorial systems, simulations, and programs, microworlds, electronic collaborations or teamwork tools, and multimedia and audio resources.
Criticism of Moore's TDT This theory, however, has not been without criticism. Through their critical analysis of transactional distance theory, Gorsky and Caspi (2005) insist that the theory should be reduced to a single relationship: as the amount of dialogue increases, transactional distance decreases. Also, Gorsky and Caspi state that this relationship should be considered as tautology, not theory. They write: Transactional distance theory was accepted philosophically and logically since its core proposition (as the amount of dialogue increases, transactional distance decreases) has high face validity and seems both obvious as well as intuitively correct. Indeed, the philosophical impact of Moore’s theory remains. Unfortunately, however, the movement from abstract, formal philosophical definitions to concrete, operational ones caused ambiguity, at best, and collapse of the theory, at worst. (Gorsky & Caspi, 2005, pp. 9-10)
32 Although there is some controversy over whether TDT is a theory, the researcher will recognize the implications of TDT, by understanding the relationship of dialogue between instructor and student through the use of narrated multimedia instruction.
Computer Self-Efficacy Computer self-efficacy, a student’s attitude toward computers, is an important element involved in learning. Self-efficacy is defined as one’s perception of his or her ability and achievement and has been found to be one of the best predictors of academic performance and achievement (Bandura, 1977). Research in the field of self-efficacy shows that self-efficacy will influence one’s choice of whether to engage in a task, the effort used in performing it, and the persistence shown in accomplishing it (Bandura, 1977, 1982; Bandura & Schunk, 1981; Barling & Beattie, 1983; Bouffard-Bouchard, 1990; Brown, Lent, & Larkin, 1989; Hackett & Betz, 1989). For instance, students with higher self-efficacy tend to work harder and persevere longer when working on a challenging assignment. Computer experience has been shown to relate to levels of computer self-efficacy. Torkzadeh and Koufteros (1994) found that the computer self-efficacy of a sample of 224 undergraduate students increased significantly following a computer training course. In another study, researchers found a significant positive correlation between previous computer experience and computer self-efficacy beliefs (Hill, Smith, & Mann, 1987). A study on gender differences in self-efficacy and attitudes toward computers (Busch, 1995) indicated the most important predictors of computer attitudes were previous
33 computer experience and encouragement. Ertmer, Everbeck, Cennamo, and Lehman (1994) also found that positive computer experience increases computer self-efficacy. Therefore, computer self-efficacy is important in this study, since it could potentially affect learning outcomes. If students have a high computer self-efficacy score, then they might be able to learn information presented on computers more easily than students who had a lower computer self-efficacy score. Students with a lower computer self-efficacy score might also resist to learning from computers, making the multimediaenhanced instruction less likely to be successful.
Learning Styles Another concept central to the problem of determining the effectiveness of multimedia-enhanced instruction is the interaction of possible learning styles differences. In other words, might learning style differences affect student learning in a multimedia-enhanced environment and therefore affect performance? The concept of learning styles promotes the idea that instruction should be flexible enough to support different learners. Clark and Mayer (2003) insist that there is no such thing as a visual or auditory learner. They argue that we learn in essentially the same way, through building on preexisting cognitive structures and encoding this understanding into long term memory. “Accommodating different learning styles may seem appealing to e-learning designers who are fed up with the ‘one-size-fits-all’ approach and to clients who intuitively believe there are visual and auditory learners, ” Clark and Mayer tell us (2003, p. 101). Furthermore, concepts of learning styles are based upon what Clark and Mayer term the “information delivery theory” (2003, p. 101),
34 meaning that learning consists of receiving information. Although it is possible that people may have preferences for learning, the principles of cognitive psychology indicate that people learn through both auditory and visual channels. This supports the theory of multimedia learning, which is based upon Clark and Mayer’s assumptions that “(a) all people have separate channels for processing verbal and pictorial material, (b) each channel is limited in the amount of processing that can take place at one time, and (c) learners actively attempt to build pictorial and verbal models from the presented material and build connections between them” (2003, p. 102). Additionally, it has been shown that learners tend to not accurately understand or know their learning styles. In one recent study, participants were surveyed before taking a course regarding their preferences for amount of practice. They were then assigned to two online courses—one with many practice exercises and the other with half the amount of practice. Half of the learners were matched to their preferences and half mismatched. The results showed that regardless of their preference, those assigned to the full practice version achieved significantly higher scores on the post-test than those in the shorter version (Schnackenberg, Sullivan, Leader, & Jones, 1998). Although there is disagreement about learning styles, possible effects of learning styles are examined in this study. The concept that we learn in different ways is an important variable to address in the data analysis of the study and an important element to consider in the design of the multimedia instruction. Plus, online learning environments are attractively positioned to address changing or progressive learning styles, through the inherent flexibility and adaptability of the instruction. For instance, a student can select from a path of instruction without even consciously thinking of that
35 path being geared toward a learning style. A link to an audio learning lesson can provide a different learning environment than a link to a visual lesson, for instance. Or, a more difficult approach for a more skilled student can easily be added to the course content, as well as a less difficult instructional path for those in need of more help. Being conscious and respectful of learning styles was deemed to be an integral part of this study. Therefore, it was necessary to find a learning style instrument that would not only be an accurate representation of a student’s learning style, but also one that could be used in statistical analysis. This proved to be another challenge, since there are differing theories on learning styles and many online survey instruments available to supposedly measure them. A review of learning style theories follows.
Review of Learning Styles Theories There has been much research on the importance of learning styles in the design and delivery of instruction. Felder (1996) tells us that a learning style represents the particular set of strengths and weaknesses that individuals use as they absorb and process information. When teachers differentiate instruction to accommodate all learning styles, they can more closely match the learning preferences of students. Matching the learning styles with the appropriate instructional styles increases a student’s opportunity to learn (Vincent & Ross, 2001). There are numerous examples of learning style models which measure a wide range of factors, from whether the learner prefers information presented visually or verbally, through a global perspective or a more linear approach, or in a competitive or collaborative way. Two of the oldest models of learning styles are Witkin’s Field
36 Dependant/Field Independent Model (Witkin et al., 1954) and the Myers-Briggs Type Indicator (Soles & Moller, 1995). Field dependant learners are externally motivated and enjoy working collaboratively. On the other extreme, field independent learners are those who are intrinsically motivated, competitive, and tend to work alone. The Myer’sBriggs Type Indicator is an instrument for measuring a person’s preferences, using four basic scales with opposite poles. The four scales are: (1) extraversion/introversion, (2) sensate/intuitive, (3) thinking/feeling, and (4) judging/perceiving. This test has been the most widely used personality inventory in history. Another method of classifying learning styles is the Curry “Onion” Model (Curry, 1983), which arranges learning style models from those that focus on external conditions to those that are based on personality theory. Curry categorizes learning styles into four layers of the “onion:”
1. Instructional & Environmental Preferences are those that describe the outermost layers of the onion, the most observable traits. 2. Social Interaction Models consider ways in which students in specific social contexts will adopt certain strategies. 3. Information Processing Models describe the middle layer in the onion, and are an effort to understand the processes by which information is obtained, sorted, stored, and used. 4. Personality Models describe the innermost layer of the onion, the level at which our deepest personality traits shape the orientations we take toward the world.
37 A more recent learning styles outlook is presented by Martinez (1999), which outlines four types of learners: Transforming, Performing, Conforming, and Resistant. The Transforming learner assumes learning responsibilities and enjoys practical-based learning. The Performing learner will assume learning responsibilities in areas of interest and enjoys a combination of practical-based and theoretical learning. The Conforming learner assumes little responsibility, wants continual guidance, and is most comfortable with theoretical knowledge. The Resistant learner simply avoids learning. Richard Felder and Linda Silverman (1988) formulated a learning style model designed to capture the most important learning style differences among engineering students and provide engineering instructors with help in teaching students. They developed a survey instrument which they called the Index of Learning Styles (ILS) (http://www.engr.ncsu.edu/learningstyles/ilsweb.html). The first version of the instrument (which had 28 items) was administered to several hundred students and subjected to a factor analysis. Items that did not load heavily on one and only one item were replaced with new items to obtain the current 44-item version of the instrument. The ILS was installed on the World Wide Web in 1996. The ILS model classifies students as having preferences for one category (ranked in a straight line continuum) or the other in each of the following four dimensions: •
sensing (concrete thinker, practical, oriented toward facts and procedures) or intuitive (abstract thinker, innovative, oriented toward theories and underlying meanings);
•
visual (prefer visual representations of presented material, such as pictures, diagrams and flow charts) or verbal (prefer written and spoken explanation)
38 •
active (learn by trying things out, enjoy working in groups) or reflective (learn by thinking things through, prefer working alone or with a single familiar partner); and
•
sequential (linear thinking process, learn in small incremental steps) or global (holistic thinking process, learn in large leaps). (Felder & Spurlin, 2005, p. 103) For the purpose of this study, the visual/verbal dimension would be most
appropriate to examine, since this aligns most closely with the theoretical framework of the study, specifically Mayer’s theory of multimedia learning and Paivio’s dual-coding theory.
Instructional Interactivity Instructional interactivity is a necessary component in the design of the multimedia instruction for this study. It important to note the difference between interactivity and instructional interactivity. Allen (2003) tells us that instructional interactivity is defined as “interactions that actively stimulates the learner’s mind to do those things that improve ability and readiness to perform effectively” (p. 255). In other words, instructional interactivity invites the learner to practice new skills or discover something new. Instructional interactivity is not pushing buttons and interacting with graphics or animated effects. Rather, it is composed of four essential components, integrated in “instructionally purposeful ways” (Allen, 2003, p. 255), which are also illustrated in Figure 3.
39
Four Essential Elements of Instructional Interactivity •
Context: the framework and conditions
•
Challenge: a stimulus to action within the context
•
Activity: a physical response to the challenge
•
Feedback: a reflection of the effectiveness of the learner’s action
Figure 3. Essential elements of instructional interactivity.
Therefore, a multimedia-enhanced lesson that demonstrates instructional interactivity would include a context that would encourage learners to enter in, providing the background needed for the activity. To provide an example based upon the multimedia instruction planned for this study, the challenge could be identified as “create a new database, name it ‘Delegates’ and save it to a folder.” The activity would be for the learner to figure out how to do this, with built in verbal and/or textual prompts, and the feedback would naturally include a confirmation that this had been
40 done correctly. One type of software used for this study, Macromedia Captivate, includes the ability to create simulations, where the learner interacts with the software. The software continues as the user correctly interacts with it. Instructional interactivity fits in well with Mayer’s multimedia principles and motivation principles discussed earlier. Identifying and including instructional interactivity within multimedia-enhanced instruction will further strengthen pedagogical design and potential of the study.
Epistemological Underpinnings of Learning with Technology Cognitive learning theory and constructivism support much of the research behind multimedia learning theory and learning with technology. To provide a more comprehensive understanding of how learning unfolds in a technology-enhanced environment, a discussion of both theories follows. Constructivism could be considered an epistemology, a meaning-making theory that offers the explanation of how we know and learn (Abdal-Haqq, 1998; MacKinnon & Scarff-Seatter, 1997). Constructivists posit that knowledge is acquired through experience with content, rather than through imitation and repetition. The theory of constructivism forwards the concept that individuals are believed to “construct” knowledge in their own minds. Consequently, learning also needs to be meaningful: “If an answer cannot be retrieved, one can be constructed by bringing inferential processes to bear on activated knowledge so that a plausible answer is generated” (Gagne, Yekovich, & Yekovich, 1993, p. 118).
41 Constructivists suggest that knowledge results from a continuous process, and is tested and rebuilt by the learner. Building knowledge structures, therefore, can be viewed as a “negotiated” process where knowledge can be constructed and deconstructed in multiple ways, depending upon the context from which it is viewed (Bodner, Klobuchar, & Geelan, 2000; Jonassen, 2000). Active student engagement, inquiry, problem solving, and collaboration are some of the instructional outcomes of this learning theory. In constructivism, “correct” answers are de-emphasized, with the teacher becoming more of a guide or facilitator. Constructivists also maintain that the constructivist model produces more internalized thinking and consequently deeper understanding than traditional methods. Constructivism encourages multiple responses, knowing that the structure of the learning environment will provide feedback to the solution that fits the problem the best. The role of elaboration is important in both constructivism and cognitive theory. Being able to connect prior knowledge structures and elaborate on them is shown to be essential for new learning. Constructivists acknowledge the importance of prior knowledge and attaching it to various ideas and situations in order to make meaning. Studeis in which researchers increase elaborations, both in number and type, improved the efficacy of learning (Gagne, Yekovich, & Yekovich, 1993). Cognitive theory also stresses the importance of using examples in developing a student’s schema formation, since it is difficult to form schemas from an abstract definition. A schema is an internal knowledge structure. New information is compared to existing cognitive structures called schema. Schema may be combined, extended or altered to accommodate new information. Therefore, simply telling students about a
42 concept or idea is not enough to solidify learning, since schema production must be individually constructed within a student’s mind. Constructivists use this approach to learning as well, encouraging the student to build his or her own schema based upon connections made between new and prior knowledge. Mayer (2002, 2003) uses cognitive learning theory as a framework for his multimedia learning principles. Cognitive learning theory helps explain learning in the following ways: •
Human memory has two channels for processing information: visual and auditory.
•
Human memory has a limited capacity for processing information.
•
Learning occurs by active processing in the memory system.
•
New knowledge and skills must be retrieved from long-term memory for transfer to the job. Therefore, the theoretical constructs of constructivism and cognitive learning
theory is the cohesive framework which supports the use of Mayer's multimedia learning principles.
Additional Research on the Effects of Multimedia in Online Learning New studies are emerging that support the use of online learning elements. For instance, Carswell, Thomas, Petre, Price, and Richards (2000) found that using email and newsgroups versus using regular mail and telephone in a distance-learning course resulted in comparable learning outcomes. Gretes and Green (2000) found that augmenting a lecture course with online practice quizzes resulted in better performance
43 on examinations. Bork (2001) also suggests that engaging computer-based and computer-mediated interactions facilitates learning. Herrington and Oliver (1999) observed higher-order thinking in students’ talk when using an interactive multimedia program. A study comparing learning outcomes of online multimedia and lecture versions of an introductory computing course found that the online students outperformed the lecture students in applied-conceptual learning (Kekkonen-Moneta & Moneta, 2002). Another study (Kettanurak, Ramamurthy, & Haseman, 2001) found that interactivity positively influenced learner attitudes, which enhanced learner performance. Frear and Hirschbuhl (1999) observed that the use of interactive multimedia enhanced problem-solving skills. McFarland (1996) also concurred with these studies, concluding that the proper use of multimedia can enhance learning. Research is consistently demonstrating that people learn more deeply from words and pictures than from words alone. In various studies, researchers compared the test performance of students who learned from animation and narration against those who learned from narration alone or text and illustration alone (Mayer, 1989; Mayer & Anderson, 1991; Mayer, Bove, Bryman, Mars, & Tapangco, 1996; Mayer & Gallini, 1990; R. Moreno & Mayer, 2002). In all of the above studies, students who experienced a multimedia-enhanced lesson of words and pictures performed better on a subsequent transfer test than students who received the same instruction only in words. These research studies support Mayer’s multimedia effect—that people learn more deeply from words and graphics than from only words. These studies provide strong evidence of the positive effects of multimediaenhanced learning. Supplemented by what we now know about multimedia learning
44 through Mayer’s research, distance learning theory, dual coding theory, learning styles, computer self-efficacy, motivation principles, instructional interaction, and cognitive learning theory, it is possible to envision the development of course lessons that would include research-based multimedia elements to improve student learning in an online environment.
45
CHAPTER 3: METHODS AND PROCEDURES
Research Questions and Hypotheses Statements The availability of sophisticated multimedia software that is highly interactive has offered many options for creating engaging and sophisticated learning content and environments. But how should this software be used, when should it be used, and what are the potential benefits to student learning? This is an issue that is important to address, since multimedia instruction is costly and time-consuming to produce. Therefore, this research is focused on analyzing the effectiveness of multimediaenhanced instruction by asking the following questions: •
Does the inclusion of research-based multimedia-enhanced instruction have any significant effect on student learning in an online learning environment compared to student learning in an online learning environment without the multimedia-enhanced instruction?
•
Would learning be significantly affected by a student’s visual/verbal learning preference, computer self-efficacy, and/or experience with database software? Based on Mayer’s underlying multimedia learning theory (2003) and research on
the effects of multimedia on learning, it would be expected that students would perform better after interacting with research-based multimedia components than students who have not experienced this type of instruction. Therefore, the directional hypothesis for the first question is as follows:
46 •
There will be significant improvement in student learning in an online learning environment using research-based multimedia-enhanced instruction compared to student learning in the same online environment without the multimediaenhanced instruction.
The null hypothesis for the second question is: •
There will be no significant differences in learning outcomes based upon a student’s visual/verbal learning preference, computer self-efficacy, and experience with database software.
Research Design The researcher used a pre-test, post-test control group design, with participants randomly assigned to the experimental and control groups. Both the experimental and control groups were given the pretest, the experimental group was given the treatment, while the control group was not given the treatment. Then, both groups were given the post-test. This design was chosen since it is considered excellent for controlling the threats to internal validity (Gall, Gall, & Borg, 2003). Testing effects are thus controlled, because the experimental and control groups take the same tests. If the experimental group performs better on the post-test, this result cannot be attributed to pre-testing, because both groups had the same pre-testing experience.
Participants A sample of 60 undergraduate students was used. Participants were enrolled in four sections of an introductory educational technology course during the fall of 2005 and spring of 2006. The distribution of males to females was 23.3% males and 76.7%
47 females, a typical representation of the gender mix of education students at Boise State. Gender percentages between the two groups were almost the same and a Pearson chi-square indicated no significance in gender distribution between the groups: X2(1) = .373, p = .542. Additionally, Cramer's V was .079, close to the lower limit of zero. This indicates that the association between the type of instruction and gender is extremely weak. The age range of students was very similar between groups, with the vast majority of students falling in the range of between 20 and 30 years of age. In the control (No MM) group, this age range comprised 70% of students, while the experimental or multimedia (MM) group comprised 73.3%. Again, Pearson chi-square statistics showed no significant differences between the two groups based upon age: X2 (4) = .357, p = .986). Cramer's V was .077.
48
80
60
40
Percent
20
0
