October 2006 Volume 9 Number 4

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It is common practice to evaluate BBN predictive performance by testing the network against data that were not used for training it. This is an advantage of such data-driven approach against efficiency-centric and expertcentric approaches for development of an adaptive system (Heckerman, 1996). The effectiveness of the Bayesian Network obtained was validated by using the 10-fold cross validation method. With this method, the data population is randomly divided to training and validation data sets in a 9:1 ratio, repeated ten times. The mean performance of the developed network was found to be 88,43% (+-1,36%, p
direct experimentation with a new software environment has been recorded also by other researchers (Hellman, 1989). Some specific observations of the user study are discussed next. In some cases, (for example (a) activity of linking two objects in a model with a relation and (b) the selection of variables to create a graph), the provided help was found to be critical for successful and uninterrupted task execution. This was discovered during the user study. These problems were attributed to the fundamental gap between the designer’s conceptual model (actual system design) and the user’s conceptual model (expectations of the users referring to the actual way to carry out specific actions). On another occasion, the users experienced frustration when they repeatedly tried to change the direction of a specific relation between two objects, an action that is not directly supported by the MC. Finally, the users found the cut and paste tools and functions difficult to use. Concerning the AUSM’s presentation of information, they found that the explanations are clear and well written, but they would like to have more links to relative topics in the body of the help text. Finally, the actions carried out during the usability evaluation of the AUSM were recorded, collected and analyzed. They were analyzed against the relevance of the three help items presented to the user, concerning the actual user’s next action. The results were in line with the preliminary evaluation of AUSM. The AUSM presented first the desired help item in the 40,181% of analysed actions, as second in the 27,765% of actions and as third most likely in the 5,869% of cases, with a total forecast success 73,815%. The analysis of the events seems to be in line with the user’s subjective evaluation of the AUSM. Using Bayesian Networks for automatic classification of problem solving strategies In this section, development of a tool for automatic classification of problem solving strategies is described. The source data for BBN construction were captured from user observation sessions of 30 high school students while using the C.AR.ME microworld. This environment is an open interactive system using multiple representations for learning concepts of geometry, i.e. equivalent shapes and surface measure methods, (Kordaki and Potari, 1998). The experiment concerned activities for solving two distinct problems: (a) transformation of a nonconvex polygon into a geometrically equivalent shape and (b) surface comparison of a non convex polygon with a square. Students were asked to solve these two problems with all the possible approaches that might find appropriate, using the tools offered by the microworld. The analysis of the problem solving strategies expressed by the students is extensively described in the work of Kordaki and Potari, (1998). The different problem solution strategies that the students used were classified in thirteen (13) categories. The most common strategy was described as “Using the measurement function”, that was applied by 23.7% of the students, while the “Using the automatic measurement function” was applied by 18.4% . These two strategies involved direct use of provided tools. Other strategies involved direct manipulation of shapes and combination of tools with direct manipulation and transformation of the drawn shapes. 114 log files have recorded the sequences of operations of the 30 students to solve the given problems. Each one of them was related to a specific problem solving strategy. These log files were used to construct the BBN. In most of these log files, extensive comments were associated, describing the strategy expressed by the student, accompanied by a screenshot showing the final state of the microworld, at the end of the problem solving process. These log files were analysed using the Usability Analyzer (Tselios et al., 2002), a tool supporting analysis of log files captured from educational environments. In this tool, the evaluator can review the interaction process, by observing simultaneously the captured log files, comments taken during the field study and recorded screenshots showing the student’s interaction with the educational software. The next phase was to store the log files’ information into a table. The fields used were the 20 different low level student operations recorded and the frequency of usage for each one in every problem solving strategy. For example, Cut and Paste operations depict editing actions, MeasuringAreas depicts usage of a tool to measure the area of a specific area, and Triangle, or Rectangle operations depicts usage of automated tools to transform a geometric shape to another type of equivalent area. The hypothesis used to build the BBN and transform accordingly the interaction data, was the hypothesis that whenever a student adopts a specific strategy to solve a problem, he carries out a set of specific operations with high frequency. This kind of activity could be traced in her log file, and could help us classify automatically the problem solving strategy. Additionally information stored, referred to the total number of interaction events (sum variable) and the type of strategy used as recognized originally by the evaluator (class variable, see Figure 6). 160
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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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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
Page 149 and 150: a strong agreement between the two
Page 151 and 152: of the dynasty. 3. Information on
Page 153 and 154: Comparing works designed using the
Page 155 and 156: Tselios, N., Stoica, A., Maragoudak
Page 157 and 158: strategies during their interaction
Page 159 and 160: P(B|D) is the probability of a netw
Page 161 and 162: Online help adaptation using Bayesi
Page 163: AUSM that will return the most prob
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
Page 195 and 196: course and again at the end of the
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
Page 205 and 206: Researchers, for review and coding
Page 207 and 208: quality that are the focus of the M
Page 209 and 210: Kelley, M. A., Whitaker, S. D., Nee
Page 211 and 212: or underserved populations. For exa
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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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October 2006 Volume 9 Number 4

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