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.
Index Terms—Learning factor analysis, machine learning,
SHAP values, PISA.
The authors are with Alef Education, UAE (e-mail:
ali.nadaf@alefeducation.com).
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.
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).