Abstract
There are numerous algorithms and tools to help an expert map exercises and tasks to underlying skills. The last decade has witnessed a wealth of data driven approaches aiming to refine expert-defined mappings of tasks to skill. This refinement can be seen as a classification problem: for each possible mapping of task to skill, the classifier has to decide whether the expert’s advice is correct, or incorrect. Whereas most algorithms are working at the level of individual mappings, we introduce an approach based on a multi-label classification algorithm that is trained on the mapping of a task to all skills simultaneously. The approach is shown to outperform the existing task to skill mapping refinement techniques.
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This work is funded by the NSERC Discovery funding awarded to the second author.
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Minn, S., Desmarais, M.C., Fu, S. (2016). Refinement of a Q-matrix with an Ensemble Technique Based on Multi-label Classification Algorithms. In: Verbert, K., Sharples, M., Klobučar, T. (eds) Adaptive and Adaptable Learning. EC-TEL 2016. Lecture Notes in Computer Science(), vol 9891. Springer, Cham. https://doi.org/10.1007/978-3-319-45153-4_13
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DOI: https://doi.org/10.1007/978-3-319-45153-4_13
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