用分段线性模型预测学生满意度，发现时间管理最关键。
Automatic Piecewise Linear Regression for Predicting Student Learning Satisfaction
- 用自适应分段线性回归建模学习满意度
- 时间管理和专注力影响最显著，参与线下课也重要
- 可个性化分析，适合教育定制化研究
尽管学习满意度已被广泛研究，但可解释机器学习与神经网络等现代技术尚未得到充分探索。本研究证明，结合提升可解释性的增强方法与自动分段线性回归（APLR）在多种先进方法中对学习满意度的预测效果最佳。通过分析APLR的数值与可视化结果，发现学生的时间管理能力、专注力、对同学的帮助感知以及参与线下课程对满意度有最显著的正面影响。令人意外的是，参与创造性活动并未带来积极影响。此外，该模型可在个体层面解析影响因素，使教育者可根据学生特征定制教学方案。
原文摘要 · Abstract (English)
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.
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