基于随机森林的翻转混合教学效果评价与提升策略
作者:
作者单位:

重庆大学土木工程学院

中图分类号:

G511

基金项目:

重庆市高等教育教学改革研究重点项目(222012); 重庆大学教学改革研究项目(2021Y37)。


Evaluation and Improvement Strategies for Flipped Blended Teaching Based on Random Forests
Affiliation:

Chongqing University

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    摘要:

    探索了一种基于机器学习的混合式教学效果评价方法,利用随机森林算法,基于成绩、行为、情感等多种类型和来源的数据,构建了混合式教学评价模型,并通过特征重要性分析提出针对性的教学提升策略。以中国大学 MOOC 开设的国家级一流本科线上课程《地学景观探秘.审美.文化》为例,收集了 376 名学生的问卷数据,进行了模型训练和验证。结果表明,优化后的随机森林模型拟合优度提高40%、残差平方均值降低7%,模型预测成绩对比学生实际成绩R2达到0.92,可有效地预测和分析学生的学习效果;学习过程参与、线上线下学习途径、手机使用、形成性评价、学习时间投入、收获感等是影响学习效果的关键因素;根据这些关键因素,提出了针对性的混合式教学效果提升策略。研究提出的方法可为优化混合式教学设计、提高混合式教学质量、促进个性化教育提供参考。

    Abstract:

    This paper explores a method for evaluating blended teaching effectiveness using machine learning. It constructs a blended teaching evaluation model with the Random Forest algorithm based on diverse data types and sources, including grades, behaviors, and emotions. Feature importance analysis was used to propose targeted strategies for teaching improvement. Using survey data from 376 students enrolled in the national top-level online course "Exploring Geoscience Landscapes: Aesthetics and Culture" on Chinese University MOOC, the model was trained and validated. Results show that the optimized Random Forest model improves fit by 40%, reduces mean squared error by 7%, and achieves an R2 of 0.92 when predicting student performance. Key factors influencing learning outcomes include participation, online-offline learning paths, mobile phone use, formative assessment, time investment, and sense of achievement. Based on these factors, targeted strategies for enhancing blended teaching effectiveness were proposed. The method provides references for optimizing blended teaching design, improving blended teaching quality, and promoting personalized education.

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  • 收稿日期:2024-09-05
  • 最后修改日期:2025-01-14
  • 录用日期:2025-03-26
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