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- W2510968082 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." @default.
- W2510968082 created "2016-09-16" @default.
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- W2510968082 date "2016-01-01" @default.
- W2510968082 modified "2023-09-27" @default.
- W2510968082 title "Refinement of a Q-matrix with an Ensemble Technique Based on Multi-label Classification Algorithms" @default.
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- W2510968082 doi "https://doi.org/10.1007/978-3-319-45153-4_13" @default.
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