Analysing Student Performance using Sparse Data of Core Bachelor Courses

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Published Feb 24, 2015
Mirka Saarela Tommi Kärkkäinen

Abstract

Curricula for Computer Science degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separated CS core studies, learning skills between these and other required studies may vary a lot, showing in student’s overall performance. To analyze such a situation, we apply nonstandard educational data mining techniques on a preprocessed log file of the passed courses. The joint variation of the course grades are studied through correlation analysis while the intrinsic groups of students are created and analyzed using a special clustering technique. Since not all students have attended all the courses, there is a nonstructured sparsity pattern to cope with. Finally, multilayer perceptron neural network with cross-validation based generalization assurance is trained and analyzed using analytic mean sensitivity to explain the nonlinear regression model constructed. Local (within-methods) and global (between-methods) triangulation of different analysis methods is argued to improve the technical soundness of the presented approaches, giving more confidence on our final conclusion that general learning capabilities predict the success of students better than specific IT skills obtained as part of the core studies.

How to Cite

Saarela, M., & Kärkkäinen, T. (2015). Analysing Student Performance using Sparse Data of Core Bachelor Courses. JEDM | Journal of Educational Data Mining, 7(1), 3-32. Retrieved from https://jedm.educationaldatamining.org/index.php/JEDM/article/view/JEDM056
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