基于在线行为的学生学习绩效预测:在COVID-19大流行期间的经验研究
Yiyi Liu1, Zijie Huang2, Gong Wang1
1Department of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
PeerJ. Computer science
|December 11, 2023
概括
动态特征,而不是人口统计,是学生在强烈的在线学习环境中的表现的关键预测因素. 教师应该专注于这些动态元素,以便在疫情期间和之后更好地指导学生.
科学领域:
- 教育技术的教育技术
- 机器学习在教育中的应用
- 数据挖掘 数据挖掘
背景情况:
- 由于COVID-19的流行,需要迅速转向强烈的在线大学教学.
- 机器学习的进步使复杂的教育数据挖掘成为可能.
- 新的在线学习功能对学生绩效预测的影响尚不清楚.
研究的目的:
- 用在线学习的背景下使用机器学习技术来预测学生的学业成绩.
- 为了确定最重要的特征,预测学生在疫情引起的在线学习条件下的表现.
- 为了比较流行病前和流行病期间的特征重要性.
主要方法:
- 利用最先进的机器学习技术来预测学生的表现.
- 应用各种特征选择技术来分析特征的重要性.
- 在大流行之前和期间的数据集中比较特征的重要性.
主要成果:
- 动态特征 (例如,在线任务完成,阶段测试) 比人口或校园属性更能预测学生表现.
- 这些动态特征对于在当前的在线学习环境中区分学生学业成果至关重要.
- 与大流行前的数据相比,特征的重要性发生了显著的转变.
结论:
- 教师应该优先考虑动态的学习行为,而不是静态的人口统计数据,以指导在线环境中的学生.
- 现有的特征需要改进,包括更精确的分类和分类的扩展.
- 未来的研究应该纳入课堂表现和主观学习理解.
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