Automatic Piecewise Linear Regression for Predicting Student Learning Satisfaction
This work addresses the problem of improving educational outcomes for students and educators by providing interpretable predictions, though it is incremental as it applies an existing method to a new domain.
The study tackled predicting student learning satisfaction by applying automatic piecewise linear regression (APLR), finding it outperformed other state-of-the-art methods and identified key factors like time management and concentration as most impactful.
Although student learning satisfaction has been widely studied, modern techniques such as interpretable machine learning and neural networks have not been sufficiently explored. This study demonstrates that a recent model that combines boosting with interpretability, automatic piecewise linear regression(APLR), offers the best fit for predicting learning satisfaction among several state-of-the-art approaches. Through the analysis of APLR's numerical and visual interpretations, students' time management and concentration abilities, perceived helpfulness to classmates, and participation in offline courses have the most significant positive impact on learning satisfaction. Surprisingly, involvement in creative activities did not positively affect learning satisfaction. Moreover, the contributing factors can be interpreted on an individual level, allowing educators to customize instructions according to student profiles.