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Combining Domain Modelling and Student Modelling Techniques in a Single Automated Pipeline
- Source :
-
International Educational Data Mining Society . 2022. - Publication Year :
- 2022
-
Abstract
- In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a concept. We conducted an evaluation using six large datasets from a Python programming course, evaluating the performance of different domain and student modelling techniques. The results showed that it is possible to develop a successful and fully automated pipeline which learns from raw data. The best results were achieved using alternating least squares on hill-climbing Q-matrices as domain modelling and exponential moving average as student modelling. This method outperformed all baselines in terms of accuracy and showed excellent run time. [For the full proceedings, see ED623995.]
Details
- Language :
- English
- Database :
- ERIC
- Journal :
- International Educational Data Mining Society
- Publication Type :
- Conference
- Accession number :
- ED624081
- Document Type :
- Speeches/Meeting Papers<br />Reports - Research