Towards Predictive Modelling of Student Affect from Web-Based Interactions

Authors:
Manolis Mavrikis;Antony Maciocia;John Lee
Affiliations:
School of Mathematics, The University of Edinburgh and School of Informatics, The University of Edinburgh;School of Mathematics, The University of Edinburgh;School of Informatics, The University of Edinburgh
Venue:
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Year:
2007

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Abstract

This paper presents the methodology and results of a study conducted in order to establish ways of predicting students' emotional and motivational states while they are working with Interactive Learning Environments (ILEs). The interactions of a group of students using, under realistic circumstances, an ILE were recorded and replayed to them during post-task walkthroughs. With the help of machine learning we determine patterns that contribute to the overall task of diagnosing learners' affective states based on observable student-system interactions. Apart from the specific rules brought forward, we present our work as a general method of deriving predictive rules or, when there is not enough evidence, generate at least hypotheses that can guide further research.