Towards Predictive Modelling of Student Affect from Web-Based Interactions

Manolis Mavrikis, Antony Maciocia, John Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationARTIFICIAL INTELLIGENCE IN EDUCATION
EditorsR Luckin, KR Koedinger, J Greer
Place of PublicationAMSTERDAM
PublisherI O S PRESS
Pages169-176
Number of pages8
ISBN (Print)978-1-58603-764-2
Publication statusPublished - 2007

Keywords / Materials (for Non-textual outputs)

  • Affective modelling
  • machine learning
  • HELP-SEEKING

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