October 2006 Volume 9 Number 4

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action and therefore to potentially provide the user with useful support in an efficient way. The proposed approach serves as a scaffolding mechanism during modelling activities. The support system helps students accomplish a complex task requiring specific actions, at critical points of their task. The proposed scaffolding mechanism, realised with the use of Bayesian Networks, does not reduce the transparency of the system. Instead, it provides the ability to the student to move ahead over potentially critical points in terms of tools operations. In the scaffolding process adopted, the system provides help to the students on those elements of tasks that are beyond their capacity, and allow them to concentrate upon the elements that are within their range of competence. This is accomplished with identification of frequent interaction patterns using Bayesian Networks and subsequent correlation with desired task accomplishment, thus enabling the system to infer the desired help item. It provides help just when it is needed thus reducing the effort to search for relevant support, while maintaining a focus on the modelling process without interruption and disorientation. In the second case of automatic classification of problem solving strategies in an open problem solving environment, the approach produced good results. The classification rate of the BBN algorithm is very high in comparison with other popular algorithms, even with a small amount of data provided. However, the BBN could be proven unsuccessful to recognize strategies with a low occurrence rate represented in the reference sample. A future research goal is to estimate the lowest possible sample for each category related to the total, which leads to unbiased and solid results. Various techniques have been proposed in the literature for tackling this problem (e.g. Daskalaki et al. <strong>2006</strong>), These could be particularly useful in our case, since often rare problem solving strategies are of particular interest and their diagnosis is required by the tutors. Another important advantage of the Bayesian Network approach is the derived graph which offers an easy to understand representation of the variables showing the nature of the interaction. Study of the graph could lead to usability improvements, as shown in the first study, by providing the most suitable help items or the possible actions related to the interaction dialogue flow and status. From an educational perspective, automated problem solving strategy recognition in open problem solving environments could substantially improve the learning process and support the tutors, when dealing with large sets of student data. That is, the educator could rapidly verify the adoption of specific problem solving strategies. In addition to this, real time implementation of the proposed approach, in the case of distance learning systems could aid diagnosing and evaluating the learning outcome and support teacher intervention in the form of adequate feedback. In general, it is argued that a more efficient and adaptive user system increases indirectly the pedagogical value of the environment because it contributes to the transparency of the tool A more intuitive flow of interaction enhances the learnability of complex tools such as those included in open problem solving environments. Our approach could be expanded to adapt the behaviour of the open educational environment in various aspects such as goal recognition, adaptive assessment regarding the expertise of the user, and adaptation of the interface (for example, adaptation of right click pull down menus, together with a constant set of commands). Further research is needed in order to investigate these areas, together with a longitudinal evaluation of the effects of the developed module on the way it affects student learning within such an environment. References Avouris, N., Tselios, N. & Tatakis E. C. (2000). Development and Evaluation of a Computer-based Laboratory Teaching Tool, Journal Computer Applications in Engineering Education, 9 (1), 8-19. Bunt, A. & Conati, C. (2003). Probabilistic Student Modelling to Improve Exploratory Behaviour. Journal of User Modeling and User-Adapted Interaction, 13 (3), 269-309. Collins, J., Greer, J. & Huang, S. (1996). Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets. In Proceedings of Intelligent Tutoring Systems, 569-577. Conati, C., Gertner, A. S., VanLehn, K. & Druzdel, M. (1997). On-line Student Modeling for Coached Problem Solving Using Bayesian Networks. In Proceedings of the 7 th International Conference on User Modeling, 231- 242. Conati, C., Gertner, A. & VanLehn, K. (2002). Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and User-Adapted Interaction, 12 (4), 371-417. 163
Cooper, J. & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9 (4), 309-347. Daskalaki, S., Kopanas, I. & Avouris, N. (<strong>2006</strong>). Evaluation of Classifiers for an Uneven Class Distribution Problem, Applied Artificial Intelligence, 20, 1-37. Dimitracopoulou, A. & Komis, V. (2005). Design principles for the support of modelling and collaboration in a technology-based learning environment. International Journal of Continuing Engineering Education and Lifelong Learning, 15 (1/2), 30-55. Ergazaki, M., Komis, V. & Zogza, V. (2005). High-school Students’ Reasoning while Constructing Plant Growth Models in a Computer-Supported Educational Environment, International Journal of Science Education, 27 (2), 909-933. Glymour, C. & Cooper, G. (eds.), (1999). Computation, Causation & Discovery. AAAI Press/The MIT Press. Gudzial, M. J. (1993). Deriving Software Usage Patterns from Log Files. Georgia Institute of Technology. GVU Center Technical Report. 93-41. Heckerman, D. (1996). A Tutorial on Learning Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research. Hellman, R. (1989). User Support: Revealing Structure Instead of Surface, Behaviour and Information Technology, 8, (6), 417-435. Holmes, G., Donkin, A. & Witten, I. H. (1994). Weka: a machine learning workbench. In Proceedings of the 2 nd Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, 357-361. Jameson, A. (1995). Numerical uncertainty management in User and Student Modeling: An Overview of Systems and Issues. In User Modeling and User-Adapted Interaction, 5 (3/4), 193-251. Jeffreys, H. (1939). Theory of Probability. Clarendon Press, Oxford. Kinshuk, P. A. & Russell, D. (2002). Intelligent and Adaptive Systems. In Adelsberger, H., Collins, B & Pawlowski, M. J. (Eds.) Handbook on Information Technologies for Education and Training, Germany: Springler-Verlag, 79-92. Komis, V., Dimitracopoulou, A., Politis, P. & Avouris, N., (2001). Expérimentations exploratoires sur l’utilisation d’un environnement informatique de modélisation par petits groupes d’élèves, Sciences et Techniques Educatives, 8, (1-2), 75-86. Komis, V., Avouris, N. & Fidas, C. (2002). Computer Supported collaborative concept mapping: Study of Interaction, Education and Information Technologies, 7 (2), 169-188. Kordaki, M. & Potari, D. (1998). A learning environment for the conservation of area and its measurement: a computer microworld. Computers and Education, 31, 405-422. Luger, G. F. & Stubblefield, W. A. (1998). Artificial Inteligence – Structures and Strategies for Complex Problem Solving, (3 rd Ed), Addison Wesley. Mayo, M. & Mitrovic, A. (2000). Optimizing ITS Behaviour with Bayesian Networks and Decision Theory. In International Journal of Artificial Intelligence in Education, 12, 124-153. Mitchell, T. (1997). Machine Learning, McGraw-Hill, New York. Nathan, M. J. (1998). Knowledge and Situational Feedback in a Learning Environment for Algebra Sotry Problem Solving. Interactive Learning Environments, 5 (1), 135-159. Niedermayer, D. (1998). An Introduction to Bayesian Networks and Their Contemporary Applications, University of Saskatchewan, Technical Report 184-3-54440 1998. 164
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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,
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Courseware development process mode
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Didactics analysis This phase consi
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Process didactics design We focus o
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The repository is organized into co
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the analysis and the design phase o
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Figure 12. Weaving content and dida
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complete user-specific (author, ins
Page 117 and 118: References ADL (Advanced Distribute
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Page 121 and 122: (see Figure 2). The incorporation o
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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
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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
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Page 147 and 148: 2001). The system thus aims to assi
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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
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Page 203 and 204: Website Design The MTP website (www
Page 205 and 206: Researchers, for review and coding
Page 207 and 208: quality that are the focus of the M
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Page 211 and 212: or underserved populations. For exa
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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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