Abstract
Detecting and automating repetitive patterns in users’ actions has several applications. One of them, often overlooked, is supporting learning. This paper presents an approach for detecting repetitive actions in students who are interacting with an exploratory environment for mathematical generalisation. The approach is based on the use of two sliding windows to detect possible regularities, which are filtered at the last stage using task knowledge. The result of this process is used to generate adaptive feedback to students based on their own actions.
This work is funded in part from the MiGen Project, grant TLRP RES-139-25-0381. Thanks to the rest of the MiGen team for support and ideas.
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Gutierrez-Santos, S., Mavrikis, M., Magoulas, G. (2010). Sequence Detection for Adaptive Feedback Generation in an Exploratory Environment for Mathematical Generalisation. In: Dicheva, D., Dochev, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010. Lecture Notes in Computer Science(), vol 6304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_19
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