C4.5: programs for machine learning
C4.5: programs for machine learning
Windows of opportunity in electronic classrooms
Communications of the ACM
Machine Learning
One for all and all in one: a learner modelling server in a multi-agent platform
UM '99 Proceedings of the seventh international conference on User modeling
Machine Learning
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Developing Adaptive Internet Based Courses with the Authoring System NetCoach
Revised Papers from the nternational Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Detecting the Learner's Motivational States in An Interactive Learning Environment
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Classifying learner engagement through integration of multiple data sources
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Assessment of motivation in online learning environments
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Detection and analysis of off-task gaming behavior in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Prevention of off-task gaming behavior in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Cross-system validation of engagement prediction from log files
EC-TEL'07 Proceedings of the Second European conference on Technology Enhanced Learning: creating new learning experiences on a global scale
Content-free collaborative learning modeling using data mining
User Modeling and User-Adapted Interaction
In the zone: towards detecting student zoning out using supervised machine learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Gaze tutor: A gaze-reactive intelligent tutoring system
International Journal of Human-Computer Studies
User Modeling and User-Adapted Interaction
Advances in Human-Computer Interaction - Special issue on User Assessment in Serious Games and Technology-Enhanced Learning
International Journal of Technology and Human Interaction
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Most e-Learning systems store data about the learner's actions in log files, which give us detailed information about learner behaviour. Data mining and machine learning techniques can give meaning to these data and provide valuable information for learning improvement. One area that is of particular importance in the design of e-Learning systems is learner motivation as it is a key factor in the quality of learning and in the prevention of attrition. One aspect of motivation is engagement, a necessary condition for effective learning. Using data mining techniques for log file analysis, our research investigates the possibility of predicting users' level of engagement, with a focus on disengaged learners. As demonstrated previously across two different e-Learning systems, HTML-Tutor and iHelp, disengagement can be predicted by monitoring the learners' actions (e.g. reading pages and taking test/quizzes). In this paper we present the findings of three studies that refine this prediction approach. Results from the first study show that two additional reading speed attributes can increase the accuracy of prediction. The second study suggests that distinguishing between two different patterns of disengagement (spending a long time on a page/test and browsing quickly through pages/tests) may improve prediction in some cases. The third study demonstrates the influence of exploratory behaviour on prediction, as most users at the first login familiarize themselves with the system before starting to learn.