Abstract
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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Guzmán, E., Conejo, R., Gálvez, J. (2010). A Data-Driven Technique for Misconception Elicitation. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_23
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DOI: https://doi.org/10.1007/978-3-642-13470-8_23
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