Eliciting motivation knowledge from log files towards motivation diagnosis for adaptive systems

Mihaela Cocea, S. Weibelzahl

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Motivation is well-known for its importance in learning and its influence on cognitive processes. Adaptive systems would greatly benefit from having a user model of the learner’s motivation, especially if integrated with information about knowledge. In this paper a log file analysis for eliciting motivation knowledge is presented, as a first step towards a user model for motivation. Several data mining techniques are used in order to find the best method and the best indicators for disengagement prediction. Results show a very good level of prediction: around 87% correctly predicted instances of all three levels of engagement and 93% correctly predicted instances of disengagement. Data sets with reduced attribute sets show similar results, indicating that engagement level can be predicted from information like reading pages and taking tests, which are common to most e-Learning systems.
Original languageEnglish
Title of host publicationUser Modeling 2007: 11th International Conference, UM 2007, Corfu, Greece, June 25-29, 2007, Proceedings
EditorsC. Conati, K. McCoy, G. Paliouras
Number of pages10
ISBN (Print)9783540730774
Publication statusPublished - 2007
EventUser Modeling -
Duration: 1 Jan 2007 → …

Publication series

NameLecture notes in computer science


ConferenceUser Modeling
Period1/01/07 → …


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