Mining the impact of course assignments on student performance

Document Type

Conference Proceeding

Publication Date


Publication Title

Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013


Educational model for higher education has shown a drift from traditional classroom to technology-driven models that merge classroom teaching with web-based learning management systems (LMS) such as Moodle and CLEW. Every teaching model has a set of supervised (e.g. quizzes) and/or unsupervised (e.g. assignments) instruments that are used to evaluate the effectiveness of learning. The challenge is in preserving student motivation in the unsupervised instruments such as assignments as they are less structured compared to quizzes and tests. The research applies association rule mining to specifically find the impact of unsupervised course work (e.g. assignments) on overall performance (e.g. exam and total marks).



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