学生的学习行为在编程教育分析:从和社区检测的洞察力.
Tai Tan Mai1,2, Martin Crane1,2, Marija Bezbradica1,2
1School of Computing, Dublin City University, Collins Ave Ext, Whitehall, D09 Y074 Dublin, Ireland.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
度指标揭示了编程学生的不同学习行为. 高绩效的学生表现较低的学习波动,更快地实现稳定的参与,帮助教育工作者早期干预策略.
科学领域:
- 教育技术的教育技术
- 计算机科学教育计算机科学教育
- 学习分析学习分析
背景情况:
- 在编程课程中,高退学率需要更好地监测学生的参与度.
- 学习管理系统 (LMS) 的数据为学生的行为和表现提供了洞察力.
- 高维度的LMS数据提出了分析和解释性挑战.
研究的目的:
- 引入基于的指标来表示学生的学习行为.
- 分析高绩效和低绩效学生社区的学习行为.
- 为了检查COVID-19流行病对学生学习行为的影响.
主要方法:
- 利用基于的指标来量化学生的学习行为.
- 应用社区检测方法来分析学生群体.
- 分析了3个学年391名软件工程学生的经验数据.
主要成果:
- 表现更高的学生社区显示出较低的波动性.
- 在表现最好的群体中的学生更早达到稳定的学习状态.
- 该研究确定了可能与COVID-19大流行相关的学习行为转变.
结论:
- 作为教育工作者监测学生进步的有价值,可解释的指标.
- 度指标可以提高对学生参与度的理解,并促进及时干预.
- 这种方法为分析教育环境中复杂的学习行为提供了一种新的方法.
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