Dynamic Learning Framework Assessment and ...

DRAFT Proceedings of the ASME 2013 Fluids Engineering Summer Meeting FEDSM2013 July 7-11, 2013, Incline Village, Nevada, USA

FEDSM2013-16257 TOUCHPAD IN EDUCATION: DYNAMIC LEARNING FRAMEWORK ASSESSMENT AND CONTENT DEVELOPMENT FOR THE UNDERGRADUTE FLUID MECHANICS V. Kumar* , C. Ramana, S. Afrin, and J. Ortega Department of Mechanical Engineering The University of Texas at El Paso, El Paso, TX 79968 * Corresponding author: [email protected]

ABSTRACT This paper presents a dynamic learning framework (DLF) based on dynamic course contents and assessment methods using latest web-based technologies with keeping in mind the recent advancement in touchpad computing devices (such as IPAD or Android based tablets). In the DLF framework, the effectiveness is assessed via evaluating the learning outcomes of increasing the learnability of high level concepts of Bloom‘s Taxonomy of cognitive learning for the UG Fluid Mechanics. One of the major challenges is creating a fluid mechanics module that incorporates all levels of Bloom‘s cognitive taxonomy. This is achieved via integration of mathematical, conceptual and visual contents. The lower level concepts (i.e., Remembering, Understanding and Applying) are computerized and tested using Computer Adaptive Testing algorithm. Our targeted audiences are from a predominantly Hispanic cultural setting and in undergraduate mechanical engineering courses. To capitalize on unique cultural setting and linguistic needs, the assessment is prepared in bi-lingual (Spanish and English) with localized problems. A pre-assessment of students‘ learning styles was performed to assess their learning preference and the presentation was tuned to average audiences. It was observed that about 10% of the students used bi-lingual instructions in the exam which was conducted as an extra-credit option to paper based exam in order to assess the DLF framework. Students were also asked to contribute questions to generate a question database with localized problems. INTRODUCTION Fluid mechanics involve all concepts from the Science, Technology, Engineering and Mathematics (STEM) fields. In STEM education, several barriers plays important role in keeping students engaged and interested in learning the concepts. These barriers are due to several modern days issues that includes (but not limited to) cost of education, lack of advanced compatible technology that are easy to be adapted by

Neelam Agarwal Disabled Student Services Office The University of Texas at El Paso

Victor Udoewa Google London Google Inc.

faculty/teaching instructor into the framework in which students can enjoy the learning experiences. Modern day advance technology however has capability to revolutionize the STEM education, and Fluid Dynamics in particular due to it‘s wide practical relevance in our every day-life, by addressing the various learning styles, culture/linguistic, practical relevance and other learning barriers. The investigator has researched the existing computerized based learning framework/contents. Most famous online learning initiatives are the CMU‘s Open Learning Initiative (OLI), the Harvard‘s Open Courses for Free, and the MIT‘s OpenCourseWare. They provide an exhaustive and economical (or free) way of learning STEM courses. However, the main focus the proposed DLF here is the ease-of-access and dynamic integration of STEM course content and assessment development via touchpad technologies. The long-term goal of the DLF is to dilute the barriers that prevalent in the current STEM education system such as linguistic (here, only Spanish and English is considered which can be expanded to other languages in future with Google‘s automatic language translation tools), cultural (mathematics, geometry or physics are universal), and economic. Google‘s many Open-Source (such as Android development) and educational initiatives are aligned with the investigator‘s long term educational missions and there the investigator believes that Google and the investigator could be long term partner this STEM education-for-masses cause. The DLF main goal is to incorporate various degrees and preferences of learning needs by addressing the various learning barriers as described in Figure 1. The barriers to learning could exists in various form including (but not limited to) linguistic, cultural, disabilities (or student with different abilities), styles, self-efficacy (or lack of motivation), prerequisites (or a prior educational training). Many of these barriers are dynamic and can be addressed by interventions such as tutoring or allowing more time to students to finish an exam). Many of the barriers also vary from student to student

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and hence require individual attention. As summary of learning barriers are described below. In order to adapt learning for students with diverse learning styles it is important to engage participants in a variety of learning activities that use multiple senses. Consider using videos, group discussions, mini-lectures, case studies, questions and answers, panels, and role-playing. Dunn and Griggs (2000) describe learning style as an individual way to concentrate on, process, internalize, and remember new information and skills. Every individual learns differently and thus has a unique learning style. Wooldridge (1995) describes learners with the auditory, visual, and kinesthetic preferences. Auditory learners learn best by listening and talking aloud. They are good at remembering things that they hear so they learn better by listening to verbal instruction such as a lecture, discussion, or recording Coker (1996). Visual learners use vision for their primary perceptual preference and can remember most easily what they read or observe. Kinesthetic learners are most successful when totally engaged with the learning activity. They acquire information fastest when participating in a science lab, drama presentation, simulations, field trip, or other active activity. Low expectations and lack of encouragement from educators, counselors, parents, and others with whom they interact can negatively impact the performance of the students to do well in STEM majors (National Science Foundation, 2000; Seymour & Hunter, 1998). Stake and Mares (2005) found, in a study of a summer science enrichment program, that encouragement from significant people (e.g. family, teachers, peers) leads to positive attitude toward science and their science abilities. The authors hypothesize that the absence of such involvement and support can reduce students‘ feelings of science self-efficacy through virtual mode. One significant indicator of student engagement, success with, and persistence in science is a students‘ science self-efficacy (Britner and Pajares, 2006). In other words, while skills and knowledge are important factors leading to success, students also need a sense of confidence in their ability to use their skills as that leads to motivation to achieve their goals, access support when needs, more resilient when faced with challenges and engage in learning (Bandura, 1994; Kitsantas & Zimmerman, 2009). Self-efficacy is defined as judgments regarding one‘s ability to organize and execute the courses of action necessary to attain a specific goal (Bandura, 1997). Bandura (1997) defined four ways for developing a strong sense of selfefficacy, mastery experiences, social modeling, social persuasion, and physical and emotional states. Collins (2003) in her white paper for the National Action Council for Minorities in Engineering supports Bandura‘s social cognitive theory as well as identifying environmental factors that support persistence in STEM. Collins described the role played by in terms of family acceptance and encouragement [social persuasion], early and continued training in mathematics and science and their prerequisites [mastery experiences], a supportive educational environment, science training that

engages students and relates to their interests, availability of role models and mentors [social modeling], assistance in designing education and early exposure to career paths, adequate financial resources, collegial employment settings, and commitment by the larger community. In terms of interventions to boost self-efficacy it is important to have encouragement from parents, teachers, and peers whom students trust can boost confidence. When one is persuaded that he/she is capable, then one is more likely to put forth and sustain greater effort. Students with disabilities what they lack is the belief that they are capable of attaining STEM goals— such as good grades, majors, or STEM professions which leads to decreased interest in pursuing STEM (Eccles, 1994). Thus, on average, individuals with high science, technology, engineering, and/or mathematics (STEM) self-efficacy perform better and persist longer in STEM disciplines relative to those lower in STEM self-efficacy (Eccles, 1994; Seymour & Hewitt, 1997). According to Burgstahler, 2005, peer and mentor support have been identified as interventions that can increase the academic success of various groups such as students with disabilities in academic and career fields in which they have been underrepresented. Students with disabilities face challenge in relating with their peers. Thus some students with disabilities experience isolation due to being not accepted by their peers (Seymour & Hunter, 1998). Individuals with disabilities are often isolated from potential role models, mentors, and peers who face challenges at school or work that are similar to their own (Brown & Foster, 1990). Peers are particularly important and influential during this adolescence life stage when individuals' self-concepts become integrated sets of beliefs that include morals and personal choices (Jacobs, Bleeker, & Constantino, 2003). Forming peer support groups and mentor relationships can be problematic for students with disabilities as they face barriers to social activities such as ability to speak, unavailability of accessible transportation, need for an interpreter or a personal assistant, inaccessibility of buildings— inhibit connections between students with disabilities and others. Having a social support from peers can ease the transition period following high school, when a student's structured environment ends and many support systems are no longer in place (Stainback, Stainback, & Wilkinson, 1992).Social isolation has a negative effect on personal as well as academic and career success (Seymour & Hunter; Smith & Nelson, 1993). According to Agarwal, 2011, our culture shapes how we see the world and make sense of it. Culture influences all of our behaviors and interactions. Our culture also mediates how we make sense of disability and respond to people with disability. Many of the cultural elements such as language, family systems, gender roles, cultural values, beliefs, and practices have also been found to play significant roles. Recent research suggests that disability in combination with other characteristics (e.g., race and class) has a much more powerful effect on educational attainment than any one of these characteristics alone (Coutinho, Oswald, & Best, 2002).

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Cultural background is also relevant to the adult roles, career interests and preferences that a student with disability may aspire to. Students with disabilities who also come from CLD backgrounds may be at even greater risk of social isolation, due to a mismatch between their home and community culture and that of the postsecondary institution (Feagin & Sikes, 1995; Fries-Britt & Turner, 2002). Latina, and many non-Western cultures encourage values that are different from professionally-defined, Anglo-Western self-determination values (Frankland, Turnbull, Wehmeyer, & Blackmountain, 2004; Garcia, Perez, & Ortiz, 2000). For example, contextual variables that effect self-determination values can include country of origin, school environment, family and individual beliefs, neighborhood, religious beliefs, socioeconomic status, group affiliations, and parent education (Wilder et al., 2001) Research has shown that family provides a social, psychological and material support to students with disabilities. Positive feedback and encouragement from significant others such as family members builds self-efficacy as parents are one of the most important source of social persuasion, or support. Parents‘ encouragement and expectations have been shown to be a more important predictor of a child‘s self-efficacy than a child‘s own involvement in the activity (Vekiri & Cronaki, 2008). More specifically, mothers‘ beliefs regarding their children‘s ability to succeed in math careers was significantly related to the children‘s later career choices (Bleeker & Jacobs, 2004). For students with disabilities at every age, family and community support is essential. Research studies have shown that for students with physical disabilities greater attachment to friends and parents was related to levels of adaptation to college (Leatherman-Sommers, 1999). Smith and Nelson (1993) identified four social support factors were identified by disabled students as crucial: there were found to be, in the order of importance; family support, interaction with peers, interaction with faculty and, lastly, university support services. In the study Agarwal (2011), participants in this study also identified the existence of natural supports (i.e., family) and family ties played a large role in their success at the postsecondary level. METHOD Machine learning techniques, particularly in neural networks applied to pattern recognition, robotics, and document recognition have been studied for several years to successfully design algorithms for learning-from-data (LeCun, et.al, 1998, Seung, et.al, 1992, Cortes, et.al, 1994). The gradient-based concept (which has tested in machine learning context) is used

for driving the course contents (see Figure 2). The DLF will compute the learning gradient using the correctly answered questions presented in the ―test‖ windows based on the material present in the ―presentation‖ window. The gradient-based learning procedures have been used since the late 1950‘s but their usefulness for teaching interactive concepts in STEM disciplines are not widely researched. The framework therefore proposes to identify and develop exhaustive input parameters (i.e. Interventions) that will affect the hidden parameters (i.e. Barriers). The impact of barriers on individual‘s learning preferences will assessed by selectively implanting the interventions strategies. The technological advancement in touchpad (such as Android based tablets, IPAD, etc.) and cloud based computing technologies and their affordability to common people has opened new opportunities for educationist to address various barriers associated with learning by integrating and balancing various learning styles. However, we believe that the advancement in teaching practices has not caught up with technology primarily due to lack of technology friendly multimedia rich dynamic learning materials (e.g., books, customizable softwares, easy-to-use computerized adaptive testing software). The dynamic learning framework (DLF) aims to integrate the technology and provide educator an easy-to-use technology for incorporating different learning styles and will build upon the investigator‘s ongoing work on with IBM. Factors related to mismatch between the common learning styles of engineering students and traditional teaching styles of engineering professors amplified by their negative perceptions of campus climate of engineering disciplines are some of the major hurdles that affect the graduation rate or completely prevent students to participate in the STEM fields. The study is expected to addresses these challenges by integrating the latest technologies and teaching methods. The goal of the framework is to address the need for  creating a virtual dynamic learning framework using inductive and gradient-based learning techniques,  designing a coupled interactive learning and  testing fluid dynamics concepts using computerized adaptive testing algorithms,  examining various-level of difficulty concepts ranging from least difficult (level – 1) to most difficult (level -4) concepts, and  assessing its effectiveness for various concepts used in fluid mechanics.

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5. Evaluating 4. Analyzing

Learning (Students) Barriers

Bloom‘s taxonomy of cognitive learning Higher order thinking skills

6. Creating

Perception: Sensing | Institutive Understanding: Sequential | Global Processing: Active | Reflective Input modality: Verbal | Visual

Dynamic Learning Framework (DLF) A web-based computerized predictive and adaptive learning framework

Fluid mechanics module (Virtual Teacher) 3. Applying 2. Understanding 1. Remembering

Virtual Interventions

Lower order thinking skills

The DLF with Touchpad‘s electronic note taking capability will enable an instructor to focus on higher order of thinking skills (usually missing in traditional instruction method) whereas Cloud-computing instructions will replace the lower level.

Role of Parents/peers Prerequisite Self-efficacy Culture & Language

Quick & Easy to use content via TOUCHPAD with Cloud Computing Multiple means of presentation Repeated exposer to career choices

Inquiry based Self-paced Activity based Role-model Relevance

Figure 1: A schematic design of the virtual Dynamic Learning Framework (Right) integrated with on Bloom‘s taxonomy of cognitive learning

UNDERGRADUATE FLUID MECHANIC EDUCATIONAL PEDAGOGIES DEVELOPMENT The Fluid dynamics course provides an ideal test ground for testing the DLF. The Fluid dynamics is a required course for the mechanical, civil, and chemical engineers; involves rigorous visual, physical and mathematical (well known as the NavierStokes‘ equations which is a set of non-linear partial differential equations) contents; and encompasses many real life applications ranging from energy (e.g. wind, solar-thermal, geothermal) to defense (e.g. aerodynamics of fighter planes, bioterrorism, directed energy) to bio-engineering (e.g. blood clothing in DVT, hearth-pumps, drug-delivery). Due to richness of the fluid mechanics subject, the DLF has potential to provide a customized (via virtual content delivery method of the DLF) course content, instruction, and assessment method that suit students‘ interests and passions and which is otherwise impossible in a traditional lecturing method for a large class (the DLE testing is done for a class size of 60-100 students). The DLF allows the instructor to focus on the higher level of concepts. The lower level concepts (i.e., Remembering, Understanding and Applying) are computerized and tested using Computer Adaptive Testing algorithm. The example

concepts for testing the lower order skills of Bloom‘s cognitive learning are: Remembering  Recall definitions - density, temperature, conservation principles, volume, integral, and differentiations),  Replicate ideal gas law, mass-conservation laws, forcebalance laws, energy balance laws, etc.), Understanding  Control volume approach of conservation principles, Reynolds Transport theorem, Bernaulli‘s laws, Energy balance and efficiency, Dimensional analysis, internal flow Applying  Compute hydrodynamic forces, average mass flow rate, angular momentum, energy efficiency, blockage factor, etc. The high-order skills (Analyzing, Evaluating, and Creating) required human interventions. The testing higher level concepts are Analyzing:  Explain why laminar flow calculation doesn‘t match with experiments.  Predict the velocity at the center of the pipe, and interpret parabolic profile.

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

Devise mathematical representation of flow past an airplane - Navier-Stokes equations. Evaluating  Choose a heat exchanger that will give a best heating output for water flowing through it at 90oF at 1 Kg/s at a lowest cost.  Critique a project report on flow through pipe using the FLUENT software and mismatch in the results with theoretical calculations.  Evaluate the limitations of Navier-Stokes equations. Creating  Design a heat exchanger with least CO2 footprint, Plan for redesigning a hot-water system at your home using Solar Thermal Energy.  Create a framework for computer modeling of an airplane.  Formulate the Navier-Stokes equations for non-Newtonian fluids. Additionally, the DLF will be designed to help many students who face challenges during introductory engineering courses. The hypothesis is that real life engineering problems will motivate students to learn the concept, e.g., showing a video of rocket launch and linking with fluid mechanics (conservation of moment concepts) and mathematics (simple differentiation and integration) can positively affections students learning interests in mathematics and also seeing the relevance. Fluid mechanics module is developed in a multi-layer approach contents (using the Bloom‘s taxonomy of educational objectives in ―cognitive domain‖) that will be based on sophistication level of the contents. The beginning layers will be the least sophisticated and mostly based on videos created by reliable sources such as Department of Energy, NASA or the U. S. Air Force. The higher layer will accordingly increase in contents, rigors and indepth resources keeping in mind on different audiences. The lower layer video would be suitable for parents and the students alike. WEBBASED DESIGN AND CONTENT DEVELOPMENT The web-based design will be idealized modern day tablets (such as IPAD or Android based tablet). We propose to use the PHP Hypertext Preprocessor in combination with PostgreSQL database and WebGL based graphics processing for driving the contents in the presentation, test, or interactive session windows (see Figure 2). All three are OpenSource software building framework that would allow to available a wider audience group. In addition, these softwares provide easy to use applets (APIs) that will speed up the software development process (Google, 2013). A schematic of the webbased design is shown in Figure 2. The webbased integration allows the content

available to wide varieties of computing devices such as IPAD, Laptop, Desktops, and even smartphones. Computerized adaptive tests: Computerized adaptive tests or CAT is a computer based test that adapts to the examinee‘s ability level (Linden, et.al, 2004, Weiss & Kingsbury, 1984, Thissen & Mislevy, 2000). The advantages of this test are that it can provide an accurate measurement of one‘s fundamental understanding of the concepts and shorten the testing for individual students. Additionally, it provides opportunity for others who need in-depth exposure to the subject being presented. In proposal, the test presented in the ―test‖ window will use CAT techniques to quiz and the level of materials present in the ―presentation‖ window will be directly related to the individual students learning capability as measured by the CAT. This shall provide a unique opportunity for those who need extra-time (self-pace learning) to comprehensively grasp the concepts of materials in the ―presentation‖ window. Mathematics concepts module: Most students face challenges during introductory mathematics. Research on these courses tells us that some introductory mathematics may serve to discourage students from earning a STEM degree as a result of highly competitive classrooms or a lack of engaging pedagogy that promotes active participation (Seymour & Hewitt, 1997). We developed an introductory level mathematics course (covering basics concepts in linear algebra, calculus, and regression techniques). The module incorporates the computerized adaptive test and interactive presentation style providing a self-paced learning framework. Linking fluid mechanics module with mathematics concept module: In addition, many students face challenges during introductory science and engineering courses. They are often referred to as ‗‗gatekeeper‘‘ courses. Most of these courses incorporate mathematics concepts that may serve to discourage students from earning a STEM degree. We propose to integrate the mathematical concept with the engineering concepts. Our hypothesis is that the mathematical concepts when combined with real life engineering problems can become great motivational materials for students to get interested. One such example is showing a video rocket launch, linking that the fundamental of fluid mechanics and mathematics (simple differentiation and integration) can positively affect students‘ learning interests in mathematics and also seeing the relevance of the subjects to the STEM field. Our approaches to present mathematics concepts are related to the conceptual ideas presented in inductive learning such as the Challenged Based Learning (CBL) and Problem Based Learning (PBL). The difference is that we propose to employ these techniques through computerized based systems.

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Dynamic Learning Framework http://DLF.utep.edu/Fluid_Mechanics Search DLF database using Google search tool

Login: Fingerprint, audio, or type

Student interaction window - required

Dynamic Learning

Teaching window - required Presentation of educational materials - Video o Role model in STEM (motivational) o Real-life applications o Career choices in STEM - Powerpoint presentation with audio/video o Student driven in-depth materials - Powerpoint presentation with audio o Student driven in-depth materials - Fluid mechanics modules using an adaptive self-paced technique (Graphical & Mathematics) - User controlled graphics - Bi-lingual (English/Spanish)

Questions related to educational materials - True/False - Multiple choices - Pattern matching - Analytical - Mathematical - Video/pictorial - Bi-lingual (English/Spanish) - Computerized adaptive test - Drawing (Tablet version)

Guiding materials based on currently watched materials + tests Student feedback window (touchpad sensitive)

Record your voice

Record your video

Write/Draw/Sketch

Figure 2: A web-based design of the Dynamic Learning Framework (This is what students will See) (Note: Background picture is PI‘s Samsung‘s Galaxy note 10.1)

CONCLUSIONS The DLF has potential to revolutionize the STEM-education. The DLF is expected to reduce the cost- of-education of higherstudies in STEM fields by minimizing the human based tutoring needs for lower-order thinking skills development (where most students perform poorly). It is also expected to improve the quality of STEM education because it allows instructors (when coupled with traditional class setting) to

focus on higher-order of thinking skills. The DLF also offers advantages over flipped-classroom settings (a preferred choice for sensing, global, and visual learning styles students) because of dynamic nature of content presentation (expected to be inclusive for the institute, sequential, verbal as well as the sensing, global, and visual learning styles students). Note: The main author is still in the process of getting the IRB approval and hence is unable to provide the findings of this

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work until an approval is granted. The scope of this paper is however limited to the dynamic learning framework development. ACKNOWLEDGMENTS This research has been partially supported by the IBM's 201112 Smarter Planet Faculty Award to Dr. Kumar. In addition, the support also was provided by the Department of Mechanical Engineering and the University of Texas at El Paso. Works Cited REFERENCES Agarwal, N. (2011). Perceptions of students with disabilities in a Hispanic serving Institution. (Doctoral dissertation). Retreived from ETD Collection for University of Texas, El Paso. Paper AAI3490100. http://digitalcommons.utep.edu/dissertations/AAI3490100 Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Bleeker, M. M., & Jacobs, J. E. (2004). Achievement in math and science: Do mothers‘ beliefs matter 12 years later? Journal of Educational Psychology, 96, 97–109. -Britner, S.L., & Pajares, F. (2006). Sources of science selfefficacy beliefs of middle school students. Journal of Research in Science Teaching, 43(5), 485-499. Brown, P., & Foster, S. (1990). Factors influencing the academic and social integration of hearing impaired college students. _Journal of Postsecondary Education and Disability_, _7_, 79-97. Burgstahler, S. (2005). Preparing faculty to make their courses accessible to all students. Journal on Excellence in College Teaching, 16(2), 69-86. Coker, C. (1996). Accommodating students' learning styles in physical education. Journal of Physical Education, Recreation & Dance. Reston, VA: AAHPERD. Collins, E.L. (2003). Preparing women & minorities for S&E. National Action Council for Minorities in Engineering. Retrieved May 8, 2012, from www.nacme.org/pdf/collinslist.pdf Cortes, C., Jackel, L., Solla, S., Vapnik, V.N., Denker, J. (1994), Learning curves: asymptotic values and rate of convergence, Advances in Neural Information Processing Systems 6, Morgan Kaufmann, San Mateo, CA. Coutinho, M.J., Oswald, D.P., & Best, A.M. (2002). The influence of sociodemographics and gender on the disproportionate identification of minority students as having learning disabilities. Remedial and Special Education, 23, 4959. Dunn, R., & Griggs, S. A. (2000). Practical approaches to using learning styles in higher education. Westport, CT: Bergin and Garvey. Eccles, T. (1994). Succeeding with change: Implementing action-driven strategies. New York: McGraw-Hill.

Feagin, J., & Sikes, M. (1995). How Black students cope with racism on White campuses. Journal of Blacks in Higher Education, 8, 91-97. Frankland, H. C., Turnbull A. P., Wehmeyer M. L., and Blackmountain, L. (2004). An exploration of self-determination construct and disability as it relates to the Dine (Navajo) culture. Education and Training in Developmental Disabilities, 39(3), 191-205. Fries-Britt, S., & Turner, B. (2002). Uneven stories: Successful Black collegians at a Black and a White campus. The Review of Higher Education, 25(3), 315-330. Garcia, S., Perez, A., & Ortiz, A. (2000). Mexican American mother's beliefs about disabilities: Implications for early childhood intervention. Remedial and Special Education, 21, 90-100, 120. Google, I. (2013, 01 01). Google APIs. Retrieved 01 01, 2013, from https://developers.google.com/products/ Jacobs, J. E., Bleeker, M. M., & Constantino, M. J. (2003). The self-system during childhood and adolescence: Development, influences, and implications. Journal of Psychotherapy Integration, 13, 33-65. Kitsantas, A., & Zimmerman, B. J. (2009). College students‘ homework and academic achievement: The mediating role of self-regulatory beliefs. Metacognition and Learning, 4(2), 1556-1623. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P (1998), Gradientbased learning applied to document recognition, Proc. Of the IEEE. Leatherman-Sommers, S. (1999). Attachment and adjustment to college among college students with physical disabilities. Dissertation Abstracts International, 61, 3570. Linden, V.D., W. J., & Veldkamp, B. P. (2004). Constraining item exposure in computerized adaptive testing with shadow tests. Journal of Educational and Behavioral Statistics, 29, 273 291. National Science Foundation. (2000). Women, minorities, and persons with disabilities in science and engineering. Washington, DC: U.S. Government Printing Office. Seung, S., Sompolinsky, H., Tishby, N. (1992), Statistical mechanics of learning from examples, Physical Review A, 45, 6056-91. Seymour, E., & Hewitt, N. (1997). Talking about leaving: why undergraduates leave the sciences. Boulder, CO: Westview Press. Seymour, E., and A. Hunter. (1998). Talking About Disability: The Education and Work Experiences of Graduates and Undergraduates with Disabilities in Science, Mathematics, and Engineering Majors. Washington, DC: American Association for the Advancement of Science. Smith, D. J. & Nelson, J. R. (1993). Factors that influence the academic success of college students with disabilities. Paper presented at the Annual Convention of the Council for Exceptional Children, San Antonio, TX. Stake, J. E. and K. R. Mares (2005). "Evaluating the impact of science-enrichment programs on adolescents' science

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motivation and confidence: The splashdown effect." Journal of Research in Science Teaching 42(4): 359-375. Stainback, W., Stainback, S., & Wilkinson. A. (1992). Encouraging peer supports and friendships. Teaching Exceptional Children, 24 (2), 6 – 11. Thissen, D., & Mislevy, R.J. (2000). Testing Algorithms. In Wainer, H. (Ed.) Computerized Adaptive Testing: A Primer. Mahwah, NJ: Lawrence Erlbaum Associates. Vekiri, I., & Chronaki, A. (2008). Gender issues in technology use: Perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers & Education, 51, 1392–1404.

Weiss, D. J., & Kingsbury, G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21, 361-375. Wilder, L. K., Jackson, A. P., & Smith T. (2001). Secondary transition of multicultural learners: lessons from the Navajo Native American experience. Preventing School Failure, 45(3), 119-124. Wooldridge, B. (1995). Increasing the effectiveness of university/ college instruction: Integrating the results of learning styles research into course design and delivery. In R. R. Sims&S. J. Sims (Eds.), The importance of learning styles: Understanding the implications for learning, course design and education (pp.49-65). Connecticut: Greenwood Press.

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