Abstract
Detecting the presence or absence of collaboration during group work is important for providing help and feedback during sessions. We propose an approach which automatically distinguishes between the times when a co-located group of learners, using a problem solving computer-based environment, is engaged in collaborative, non-collaborative or somewhat collaborative behaviour. We exploit the available data, audio and application log traces, to automatically infer useful aspects of the group collaboration and propose a set of features to code them. We then use a set of classifiers and evaluate whether their results accurately match the observations made on video-recordings. Results show up to 69.4% accuracy (depending on the classifier) and that the error rate for extreme misclassification (e.g. when a collaborative episode is classified as non-collaborative, or vice-versa) is less than 7.6%. We argue that this technique can be used to show the teacher and the learners an overview of the extent of their collaboration so they can become aware of it.
Original language | English |
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Title of host publication | Artificial Intelligence in Education - 15th International Conference, AIED 2011 |
Publisher | Springer |
Pages | 196-204 |
Number of pages | 9 |
ISBN (Print) | 9783642218682 |
DOIs | |
Publication status | Published - 2011 |
Externally published | Yes |
Event | International Conference on Artificial Intelligence in Education 2011 - Auckland, New Zealand Duration: 28 Jun 2011 → 1 Jul 2011 Conference number: 15th https://link.springer.com/book/10.1007%2F978-3-642-21869-9 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 6738 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Artificial Intelligence in Education 2011 |
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Abbreviated title | AIED 2011 |
Country/Territory | New Zealand |
City | Auckland |
Period | 28/06/11 → 1/07/11 |
Internet address |
Keywords
- Collaborative Learning
- Data Mining
- Group Modelling