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Multi-modal Sequence Mining & Analytics

Learning to collaborate is important. But how does one learn to collaborate face-to-face? What are the actions and strategies to follow for a group of students who start a task? We are interested in analysing aspects of students’ activity when working in digital ecologies enriched with sensors for identifying users, and also at  multi-display settings.

This project is seeking out to automatically  distinguish, discover and distil salient common patterns of interaction within groups, by mining the logs of students’ actions, detected speech, changes in group’s artefacts, etc. We are particularly interested in group situations where multiple people are engaged in creative tasks that require design thinking and sense making. Multiple data mining techniques have been used, including: classification, sequence pattern mining, process mining and clustering techniques.

AIED2013

For more information about the project, please, contact: Dr. Roberto Martinez-Maldoando

Key publications:

Martinez-Maldonado, R., Dimitriadis, Y., Martinez-Mones, A., Kay, J. and Yacef, K. (2013). Capturing and analysing verbal and physical collaborative learning interactions at an enriched interactive tabletopInternational Journal on Computer-Supported Collaborative Learning, ijCSCL, 8(4)455-485.

Martinez-Maldonado, R., Yacef, K. and Kay, J. (2013) Data Mining in the Classroom: Discovering Groups’ Strategies at a Multi-tabletop Environment. International Conference on Educational Data mining, EDM 2013, pages 121-128.

Martinez-Maldonado, R., Wallace, J., Kay, J., and Yacef, K. (2011) Modelling and identifying collaborative situations in a collocated multi-display groupware setting.  International Conference on Artificial Intelligence in Education, AIED 2011, pages 196-204.

Martinez-Maldonado, R., Yacef, K., Kay, J., Kharrufa, A., and Al-Qaraghuli, A. (2011) Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop4th International Conference on Educational Data Mining, EDM2011, pages 111-120, 2011. (Best Student Paper Award,

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