Clustering and Sequential Pattern Mining of Online Collaborative Learning Data
IEEE Transactions on Knowledge and Data Engineering
From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Learning analytics and educational data mining: towards communication and collaboration
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
Predicting students' final performance from participation in on-line discussion forums
Computers & Education
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In this paper, we propose to use clustering to improve educational process mining. We want to improve both the performance and comprehensibility of the models obtained. We have used data from 84 undergraduate students who followed an online course using Moodle 2.0. We propose to group students firstly starting from data about Moodle's usage summary and/or the students' final marks in the course. Then, we propose to use data from Moodle's logs about each cluster/group of students separately in order to be able to obtain more specific and accurate models of students' behaviour. The results show that the fitness of the specific models is greater than the general model obtained using all the data, and the comprehensibility of the models can be also improved in some cases.