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A Multi-granularity Pattern-based Sequence Classification Framework for Educational Data
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2016 (English)In: 3rd IEEE International Conference on Data Science and Advanced Analytics: Proceedings, IEEE Computer Society, 2016, 370-378 p.Conference paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016. 370-378 p.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-136610DOI: 10.1109/DSAA.2016.46ISBN: 978-1-5090-5206-6 (print)OAI: oai:DiVA.org:su-136610DiVA: diva2:1055492
Conference
3rd IEEE International Conference on Data Science and Advanced Analytics, Montreal, PQ, Canada, 17-19 October 2016
Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2017-02-10Bibliographically approved

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Papapetrou, Panagiotis
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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