ACcoding:用于在线法官编程的基于图形的数据集
Kairui Chen1, Fuqun Huang2, Zejing Liu1
1Beihang University, School of Software, Beijing, 100191, China.
Scientific data
|May 29, 2024
概括
我们介绍ACcoding,这是一个大型基于图形的数据集,用于编程教育,包含六年来数千名学生的数百万条记录. 这一数据集有助于理解学生的学习行为,并提高编程教育质量.
科学领域:
- 计算机科学教育计算机科学教育
- 教育数据挖掘教育数据挖掘
背景情况:
- 现有的编程教育数据集往往缺乏足够的规模和纵向数据,阻碍了对学生学习模式的深入分析.
- 有效的编程教育需要全面的数据集,用于绩效跟踪和教学提升.
研究的目的:
- 介绍ACcoding,一个新的,大规模的,基于图形的数据集,用于教育编程.
- 为在线法官平台上分析学生学习行为提供丰富的数据源.
- 支持教育工作者跟踪学生的进步,并选择有针对性的编程任务.
主要方法:
- 从学生提交的在线法官平台上构建基于图形的数据集.
- 收集了近28000名学生的400多万个任务解决记录.
- 数据跨越6年,包括4,559个编程任务.
主要成果:
- 截至2022年5月6日,ACcoding数据集包含4,046,652个任务解决记录.
- 数据集包括27,444名学生和4,559个编程任务在6年的时间.
- 用于追踪学生进度和任务选择的数据集应用程序的演示.
结论:
- ACcoding为推进编程教育研究提供了一个有价值的,大规模的资源.
- 数据集的大小和特征使得学生学习动态的详细调查成为可能.
- ACcoding为教育工作者提供了个性化学习和课程开发的工具.
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