在CS1编程课程中早期预测学生的成绩
Jose Llanos1, Víctor A Bucheli1, Felipe Restrepo-Calle2
1School of Systems Engineering and Computing, Universidad del Valle Colombia, Cali, Valle del Cauca, Colombia.
PeerJ. Computer science
|December 11, 2023
概括
早期预测学生在编程课程中的表现可以改善结果. 一个使用成绩,交付时间和尝试的机器学习模型准确地预测了CS1课程的成功.
科学领域:
- 计算机科学教育计算机科学教育
- 机器学习应用 机器学习应用
背景情况:
- 编程课程经常表现出高的学生失败率和低学业成绩.
- 早期识别有风险的学生对于及时的学术干预和支持至关重要.
研究的目的:
- 开发和评估一种机器学习模型,用于预测入门编程课程 (CS1) 中的学生表现.
- 确定关键的预测特征和最佳算法,用于早期绩效预测.
主要方法:
- 利用学生数据,包括成绩,实验室/考试交付时间和尝试次数.
- 采用八种不同的分类算法用于模型培训和评估.
- 使用精度,回忆,F1得分和曲线下面面积 (AUC) 评估模型性能.
主要成果:
- 梯度提升分类器 (GBC) 在第3周取得了最高的F1得分,为86%.
- 随机森林分类器 (RFC) 在第3周表现出强的表现,F1得分为83%.
- 该模型显示了对准确预测学生成功的早期预测的巨大潜力.
结论:
- 机器学习模型可以有效地预测学生在编程课程中的表现.
- 早期预测使得有针对性的干预措施能够改善学生的学习成果.
- 关键特征如成绩,交付时间和尝试都是有价值的预测指标.
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