My research interests revolve around how case based knowledge is created, enacted and assessed in professional educational context. My research is anchored in the context of case creation and their validation in medical case-based teaching.
The initial phase of case creation by medical students lead to issues of validity and reliability in the solution of cases. After a review of the case based learning literature I decided to study the phenomenon by designing and analysing case validation activity. This activity is modeled on the authentic case presentation practice performed by physicians. We are using a computer-based learning environment (BioWorld) to present a set of cases to expert teachers who are asked to solve the case and do a think-aloud protocol while solving the cases.
The validation activity simulates a case presentation for medical teachers. Through this validation activity we want to capture the decision making and strategies experts use to synthesize the information about a case as well as how they structure and communicate this information in both oral and written forms. Expert teachers cannot only provide us with "good" examples of contextual decision-making processes but they also provide explanations and justifications of these decisions. Moreover, during their think-aloud they also verbalize their metacognitive strategies while doing the cases. This validation activity can produce useful blueprint of case based teaching performance that can inform teaching and assessment practices.
We have developed a method for extracting knowledge and for making it visible to learners to validate their own problem solving solutions and to reflect on their own thinking. Knowledge is extracted through think aloud protocols while participants are solving a case in a computer-based hospital simulation. These protocols are then incorporated into a multi-layered diagram that is shared with each participant. The diagram is designed based on experts’ data to serve as a summary of their diagnostic reasoning about specific patient cases. The participant interacts with their own problem representations by validating and categorizing section of on their reasoning paths. In addition, the diagram and the categorization of elements are compared and combined with other participants’ data to provide a more complete overview of the diagnostic reasoning process for each case. In essence, both individual and shared problem representations can be built into this multi-level diagram. This problem space representation aims at building robust expert models.