Intelligent Tutoring Systems for Ill-Defined Domains - Philippe ...
Intelligent Tutoring Systems for Ill-Defined Domains - Philippe ...
Intelligent Tutoring Systems for Ill-Defined Domains - Philippe ...
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Experts have been found to have strong metacognitive skills [33] whereas novice<br />
learners demonstrate less planning and self monitoring [34]. Research has shown that<br />
experts use deliberate practice to improve their ability to observe, monitor and fine<br />
tune their per<strong>for</strong>mance [35]. Visualization of per<strong>for</strong>mance in sports and other<br />
per<strong>for</strong>mance-based disciplines is used to help athletes and per<strong>for</strong>mers to look back<br />
and reflect on their per<strong>for</strong>mance as well as the per<strong>for</strong>mance of others. Delayed<br />
observations provide athletes and chess players with a different perspective on their<br />
per<strong>for</strong>mance and help them adapt their specific moves according to the overview of<br />
the per<strong>for</strong>mance [35]. We anticipate that doing similar visualizations might promote<br />
discussions about problem solving processes that will enhance metacognitive<br />
processes and decrease the possibility of fragmentation of the problem solving<br />
processes.<br />
2 Context of Studies<br />
In the preliminary phase of our research, we designed an activity in which medical<br />
students had to create fictitious patient case scenarios. This activity highlighted<br />
important validity and reliability issues of case creation and resolution. When asked to<br />
solve the case they had written, participants did not arrive at the solution they had<br />
proposed initially. When testing this problem with expert clinicians, we found that<br />
their solution differed each time they solved the same case [1]. Additionally experts<br />
did not agree with each other concerning the ideal solution <strong>for</strong> a relatively simple<br />
case.<br />
To address these issues of the validity and reliability of solutions in a more<br />
systematic manner, we designed a methodology that uses visual representations to<br />
create and evaluate medical case presentation scenarios [1]. This methodology<br />
supports case-based teaching by offering a systematic procedure to: a) track and<br />
represent expert medical instructors’ solution processes and explanations, b) capture<br />
pedagogical expertise from these experienced teachers and, c) compare and combine<br />
solution paths <strong>for</strong> the representation and explanation of specific cases.<br />
In the current pilot study we needed to test the usability of these case-specific<br />
models on medical students. The purpose was to verify if the methodology and visual<br />
representation of cases was adequate <strong>for</strong> instructional use with medical students. We<br />
wanted to see if these visual representations could be understood by medical students<br />
and if they had an impact on their learning and improve their self-assessment by<br />
helping them "look back" at their own thinking.<br />
3 Study Design<br />
Using Expert Decision Maps 72<br />
In order to evaluate whether or not medical students could understand and utilize<br />
visual representations to improve their learning, we designed an experiment in which<br />
an experimental group interacted with visual representations while a control group did<br />
not. We anticipate that using visual representations of solution paths can provide a<br />
meaningful framework to synthesize, structure and communicate different levels of