doi: 10.18178/ijiet.2021.11.10.1548
Interpretable-Machine-Learning Evidence for Importance and Optimum of Learning Time
Ali Nadaf
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Abstract
This study uses a machine learning technique, a boosted tree model, to relate the student cognitive achievement in the 2018 data from the Programme of International Student Assessment (PISA) to other features related to the student learning process, capturing the complex and nonlinear relationships in the data. The SHapley Additive exPlanations (SHAP) approach is subsequently used to explain the complexity of the model. It reveals the relative importance of each of the features in predicting cognitive achievement. We find that instruction time comes out as an important predictor, but with a nonlinear relationship between its value and the contribution to the prediction. We find that a large weekly learning time of more than 35 hours is associated with less positive or even negative effect on the predicted outcome. We discuss how this method can possibly be used to signal problems in the student population related to learning time or other features.
Keywords
- Learning factor analysis
- machine learning
- SHAP values
- PISA
How to Cite
Ali Nadaf, Sebas Eliëns, and Xin Miao, "Interpretable-Machine-Learning Evidence for Importance and Optimum of Learning Time," International Journal of Information and Education Technology, vol. 11, no. 10, pp. 444-449, 2021. https://doi.org/10.18178/ijiet.2021.11.10.1548
Copyright & License
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).