Literaturnachweis - Detailanzeige
| Autor/inn/en | Picones, Gio; PaaBen, Benjamin; Koprinska, Irena; Yacef, Kalina |
|---|---|
| Titel | Combining Domain Modelling and Student Modelling Techniques in a Single Automated Pipeline [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022). |
| Quelle | (2022), (11 Seiten)
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Prediction; Academic Achievement; Learning Analytics; Concept Mapping; Mastery Learning; Scores; Programming Languages; Computer Science Education; Accuracy; Least Squares Statistics; Evaluation Methods; High School Students; Models; Computer Software |
| 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.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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