Abstract
Because of the flexibility of online learning courses, students organise and manage their own learning time by choosing where, what, how, and for how long they study. Each individual has their unique learning habits that characterise their behaviours and distinguish them from others. Nonetheless, to the best of our knowledge, the temporal dimension of student learning has received little attention on its own. Typically, when modelling trends, a chosen configuration is set to capture various habits, and a cluster analysis is undertaken. However, the selection of variables to observe and the algorithm used to conduct the analysis is a subjective process that reflects the researcher’s thoughts and ideas. To explore how students behave over time, we present alternative ways of modelling student temporal behaviour. Our real-world data experiments reveal that the generated clusters may or may not differ based on the selected profile and unveil different student learning patterns.
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Notes
- 1.
- 2.
Both courses were structured with Assignment (11,4), Book (1,1), Database (1,1), Feedback(1,1), File(6,24), Forum(7,6), Glossary(1,1), H5P(6,9), Lesson(5,8), Page(2,10), Quiz(5,10), Survey(6,4), URL(10,14), Wiki(1,1). The numbers represent the quantity of resources/activities available in course A and B.
- 3.
K-means has been implemented in Python with scikit-learn, Bisecting K-means with pyclustering, and Hierarchical clustering with scipy. For K-Means we select the number of cluster k analysing the SSE curve. For Bisecting K-Means we vary the parameters controlling the split. For the hierarchical we obtain the clusters by cutting the hierarchy w.r.t. the median value of the distance matrix.
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This work is supported by the EU H2020 Program under the scheme H2020-INFRAIA-2019-1: Research Infrastructure G.A. 871042 SoBigData++.
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Rotelli, D., Monreale, A., Guidotti, R. (2022). Uncovering Student Temporal Learning Patterns. In: Hilliger, I., Muñoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds) Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_25
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