Research output per year
Research output per year
Manolis Mavrikis, Antony Maciocia, John Lee
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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 language | English |
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Title of host publication | ARTIFICIAL INTELLIGENCE IN EDUCATION |
Editors | R Luckin, KR Koedinger, J Greer |
Place of Publication | AMSTERDAM |
Publisher | I O S PRESS |
Pages | 169-176 |
Number of pages | 8 |
ISBN (Print) | 978-1-58603-764-2 |
Publication status | Published - 2007 |
Research output: Contribution to journal › Article › peer-review