October 2006 Volume 9 Number 4

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Havista, N. & Lesgold, A. (1996). Situational effects in classroom technology implementations: Unfulfilled expectations and unexpected outcomes. In Kerr, S. T. (Ed.), Technology and the future of schooling: Ninety-fifth Yearbook of the National Society for the Study of Education, part 2 (131-171). Chicago, University of Chicago Press. International Society for Technology in Education (2000). National Educational Technology Standards for Students: Connecting Curriculum and Technology. O. R. Eugene: Author. Keller, J. M. (1983). Motivational design and instruction. In Reiguluth, C. M. (Ed.), Instructional design theories and models: An overview of their current status (383-434). Hillsdale, NY: Lawrence Erlbaum Associates. Lawson, T. & Comber, C. (1999). Superhighway technology: Personnel factors leading to successful integration of information and communications technology in schools and colleges. Journal of Information Technology for Teacher Education, 8, 41-53. Leu, D. J., Hillinger, M., Loseby, P., Balcom, M., Dinkin, J., Eckels, M., Johnson, J., Mathews, K. & Raegler, R. (1998). Grounding the design of new technologies for literacy and learning in teachers’ instructional needs. In Reinking, D., McKenna, M., Labbo, L. D. & Kieffer, R. (Eds.), Handbook of literacy and technology: Transformations in a post-typographic world (203-220). Mahwah, NJ: Erlbaum. Linn, M. C. (2000). Designing the knowledge integration environment. International Journal of Science Education, 22, 781-796. Mannaz, M. (1999). An expert teacher’s thinking and teaching and instructional design models and principles: An Ethnographic study. Educational Technology Research and Development, 46, 37-64. Marx, R. W., Blumenfeld, P. C., Krajcik, J. S. & Soloway. E. (1998). New technologies for teacher professional development. Teaching and Teacher Education, 14, 33-52. Niederhauser, D. S. & Stoddart, T. (2001). Teachers’ instructional perspectives and use of educational software. Teaching and Teacher Education, 17, 15-31. Norum, K., Grabinger, R. S. & Duffield, J. A.(1999). Healing the universe is an inside job: Teacher’s views on integrating technology. Journal of Technology and Teacher Education, 7, 187-203. OTA (Office of Technology Assessment). (1995). Teachers and technology: Making the connection. Washington, DC: Government Printing Office. Park, O. (1991). Hypermedia: Functional features and powerful ideas. NewYork: Basic Books. Roberts, S. K. & Hsu, Y. S. (2000). The tools of teacher education: Preservice teachers’ use of technology to create instructional materials. Journal of Technology and Teacher Education, 8, 133-152. Rogers, P. A. (2000). Barriers to adopting emerging technologies in education. Journal of Educational Computing Research, 22, 455-472. Scheffler, F. L. & Logan, J. P. (1999). Computer technology in schools: What teachers should know and be able to do. Journal of Research on Computing in Education, 31, 305-326. Siegel, S., & Castellan, N. J. (1988). NonparametricSstatistics for the Behavioral Sciences. New York: McGraw Hill. Small, R. V., Sutton, S., Miwa, M., Urfels, C. & Eisenberg, M. (1998). Information seeking for instructional planning: An exploratory study. Journal of Research on Computing in Education, 31, 204-215. Topper, A. (1998). Co-constructing practices of teaching with technology: Working collaboratively with TE instructors on technology adoption. Technology and Teacher Education Annual 1998, 74-748. Willis, J., Thempson, A., & Sadera, W. (1999). Research on technology and teacher education: Current status and future directions. Educational Technology Research and Development, 47, 29-45. 149
Tselios, N., Stoica, A., Maragoudakis, M., Avouris, N., & Komis, V. (<strong>2006</strong>). Enhancing user support in open problem solving environments through Bayesian Network inference techniques. Educational Technology & Society, 9 (4), 150-165. Enhancing user support in open problem solving environments through Bayesian Network inference techniques Nikolaos Tselios Human-Computer Interaction Group, Electrical and Computer Engineering Dept and ICT in Education Group, Dept of Educational Sciences and Early Childhood Education, University of Patras, Greece nitse@ee.upatras.gr Adrian Stoica Human-Computer Interaction Group, Electrical and Computer Engineering Dept, University of Patras, Greece stoica@ee.upatras.gr Manolis Maragoudakis Wireless Communication Laboratory, Electrical and Computer Engineering Dept, University of Patras, Greece mmarag@wc.ee.upatras.gr Nikolaos Avouris Human-Computer Interaction Group, Electrical and Computer Engineering Dept, University of Patras, Greece avouris@ee.upatras.gr Vassilis Komis ICT in Education Group, Dept of Educ Sciences and Early Childhood Education, University of Patras, Greece komis@upatras.gr ABSTRACT During the last years, development of open learning environments that support effectively their users has been a challenge for the research community of educational technologies. The open interactive nature of these environments results in users experiencing difficulties in coping with the plethora of available functions, especially during their initial efforts to use the system. In addition, -from the tutors’ perspective- the problem solving strategies of the students are often particularly difficult to identify. In this paper, we argue that such problems could be tackled using machine learning techniques such as Bayesian Networks. We show how we can take advantage of log files obtained during field studies to build an adaptive help system providing the most useful support to the student, according to the state of interaction. On the other hand, we attempt to support the tutor, by automating the process of diagnosing students’ problem solving strategies using Bayesian Networks. The presented approaches are discussed through examples of two prototypes that have been developed and corresponding evaluation studies. These studies have shown that the proposed approach can effectively support the tasks of students and tutors in such open learning environments. Keywords Bayesian Belief Networks, Open problem solving environments, Inference algorithms, On-line adaptation, Adaptive help, Automated problem solving strategy identification. Introduction During the last few years, a number of open problem-solving environments have been built that are based on the constructivist approach. Open problem solving environments are computer-based learning environments that let students actively explore certain concepts while they are engaged in problem solving, with emphasis in the active, subjective and constructive character of learning (Luger and Stubblefield, 1998). Compared to traditional learning environments the student’s activity cannot be reduced in a sequence of pre-defined tasks. In general, through an open problem solving environment the student is engaged in processes such as model building, investigation and reflection. As a result, students have the freedom to explore various entities in their own unique way. The open nature of these environments, results in users’ activity taking various forms and the patterns of use of the tools that are included in them not to be fully anticipated. Therefore, the complexity of such environments and the open nature of the tasks can often lead to poor usability, which can be an obstacle to obtain the expected pedagogical value from their use. ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 150
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October 2006 Volume 9 Number 4
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Guidelines for authors Submissions
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How Does Educational Technology Ben
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Adaptivity is one of the most impor
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3. Ease of editing and updating: wh
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Accessible and personalized Learnin
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From this definition, we figured ou
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Verifying contents Figure 3. The pa
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Figure 7. The ISA on line interface
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Adaptive Technology Resource Centre
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World Wide Web Consortium (1999a).
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guiding and supporting environment
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As an example, to improve the acces
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option). Although this finding migh
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of eLearning content and enhance th
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Hodgings, W. & Duval, E. (2002). IE
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the entire course of study. This sy
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information useful for the contextu
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Nr of student 25 20 15 10 5 0 a - w
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Vincenzo, a second year student, ne
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Evaluation Recently embodied conver
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The interaction may be performed on
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Poggi, I., Pelachaud, C., & de Rosi
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Lanzilotti, R., Ardito, C., & Costa
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quality in use, which is measured b
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process of designing high quality c
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eLSE methodology Designers and eval
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Execution phase Execution phase act
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on the inspection process and, cons
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Piccinini, N., & Scollo, G. (2006).
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Answers and reinforcement A.1: Use
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potential project ideas (from pure
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For each column in Table 2, the sum
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educational goals and the various r
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A new computerized Records Manageme
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eflected in artifacts and other str
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any distributed community include b
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message in order to obtain a respon
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Figure 3. Social network after intr
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increased face-to-face collaboratio
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References Bogenrieder, I. (2002).
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Donoghue, S. L. (2006). Institution
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contemplate part-time, rather than
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espective institutions. The primary
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greater flexibility in course selec
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Cost-benefit One distinct benefit o
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Resources The development of online
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courses where it is has little pote
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A fellowship programme, such as the
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Moore, M.G. (1998). Introduction. I
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Software reuse has two dimensions,
Page 103 and 104: Courseware development process mode
Page 105 and 106: Didactics analysis This phase consi
Page 107 and 108: Process didactics design We focus o
Page 109 and 110: The repository is organized into co
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Page 113 and 114: Figure 12. Weaving content and dida
Page 115 and 116: complete user-specific (author, ins
Page 117 and 118: References ADL (Advanced Distribute
Page 119 and 120: Bender, D. M., & Vredevoogd, J. D.
Page 121 and 122: (see Figure 2). The incorporation o
Page 123 and 124: Figure 4: Example of Summary Image
Page 125 and 126: The competitive nature of design cl
Page 127 and 128: Dias, L. B. (1999, November). Integ
Page 129 and 130: materials built into the system. Th
Page 131 and 132: This theory of multimedia learning
Page 133 and 134: E-Tutor (with video). Two weeks aft
Page 135 and 136: empirically tested in TML environme
Page 137 and 138: Rieber, L. P., (1990). Animation in
Page 139 and 140: Appendix B The following 44 items r
Page 141 and 142: Appendix C A Sample of Exam Questio
Page 143 and 144: Appendix D Perceived Usefulness Que
Page 145 and 146: Kirkpatrick, and Peck (2001) have a
Page 147 and 148: 2001). The system thus aims to assi
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Page 151 and 152: of the dynasty. 3. Information on
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Page 161 and 162: Online help adaptation using Bayesi
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Page 165 and 166: direct experimentation with a new s
Page 167 and 168: Figure 7. An example of use of the
Page 169 and 170: Cooper, J. & Herskovits, E. (1992).
Page 171 and 172: Goldberg, A. K., & Riemer, F. J. (2
Page 173 and 174: In addition to convenience, propone
Page 175 and 176: members increased flexibility. They
Page 177 and 178: Oppenheimer, T. (1997, July). The c
Page 179 and 180: The basis of reform in science educ
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Page 183 and 184: participants that learning as a gro
Page 185 and 186: A hierarchy of Systems Identifying
Page 187 and 188: the change agents to recognize the
Page 189 and 190: Nonis, A. S., & O’Bannon, B. (200
Page 191 and 192: maintaining the interest levels of
Page 193 and 194: Focus group interviews with our stu
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Page 197 and 198: second phase of valuing, commitment
Page 199 and 200: Kinzie, M. B., Whitaker, S. D., Nee
Page 201 and 202: Although this work is based on a st
Page 203 and 204: Website Design The MTP website (www
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Researchers, for review and coding
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quality that are the focus of the M
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Kelley, M. A., Whitaker, S. D., Nee
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or underserved populations. For exa
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2004 Baruch College Computer Center
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professional-level tools and resour
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Third, with the exception of the Om
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Hepp, P., Hinostroza, E., Laval, E.
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The AccessForAll strategy complemen
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of people with special needs, or di
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(including where their search for r
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international standard. The ISO pro
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information will be expressed using
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DC Metadata Terms: http://dublincor
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Choquet, C., & Corbière, A. (2006)
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TEL Standards, Specifications and P
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The Resources Management Process ma
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Step 1: Instantiation of the enterp
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Step 5: Instantiation of the techno
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correlation of the different viewpo
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Hummel, H., Manderveld, J., Tatters
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Karagiannidis, C. (2006). Book revi
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Glenn, L. (2006). Book review: Visu
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