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A Multi-granularity Pattern-based Sequence Classification Framework for Educational Data
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2016 (engelsk)Inngår i: 3rd IEEE International Conference on Data Science and Advanced Analytics: Proceedings, IEEE Computer Society, 2016, 370-378 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In many application domains, such as education, sequences of events occurring over time need to be studied in order to understand the generative process behind these sequences, and hence classify new examples. In this paper, we propose a novel multi-granularity sequence classification framework that generates features based on frequent patterns at multiple levels of time granularity. Feature selection techniques are applied to identify the most informative features that are then used to construct the classification model. We show the applicability and suitability of the proposed framework to the area of educational data mining by experimenting on an educational dataset collected from an asynchronous communication tool in which students interact to accomplish an underlying group project. The experimental results showed that our model can achieve competitive performance in detecting the students' roles in their corresponding projects, compared to a baseline similarity-based approach.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2016. 370-378 s.
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-136610DOI: 10.1109/DSAA.2016.46ISBN: 978-1-5090-5206-6 (tryckt)OAI: oai:DiVA.org:su-136610DiVA: diva2:1055492
Konferanse
3rd IEEE International Conference on Data Science and Advanced Analytics, Montreal, PQ, Canada, 17-19 October 2016
Tilgjengelig fra: 2016-12-12 Laget: 2016-12-12 Sist oppdatert: 2017-02-10bibliografisk kontrollert

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