A Data-Driven Technique for Misconception Elicitation

When a quantitative student model is constructed, one of the first tasks to perform is to identify the domain concepts assessed In general, this task is easily done by the domain experts In addition, the model may include some misconceptions which are also identified by these experts Identifying these misconceptions is a difficult task, however, and one which requires considerable previous experience with the students In fact, sometimes it is difficult to relate these misconceptions to the elements in the knowledge diagnostic system which feeds the student model In this paper we present a data-driven technique which aims to help elicit the domain misconceptions It also aims to relate these misconceptions with the assessment activities (e.g exercises, problems or test questions), which assess the subject in question.

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