GOAT:一种全新的全球-本地优化的图形转换器框架,用于预测学生在协作学习中的表现
Tianhao Peng1,2, Qiang Yue1,2, Yu Liang3
1Beihang University, Beijing, 100191, China.
Scientific reports
|March 22, 2025
概括
本研究介绍了GOAT,这是一个新的框架,通过分析动态交互和文本内容来预测学生在协作学习中的表现. GOAT通过捕捉空间,时间和全球-本地团队动态来增强协作学习分析.
科学领域:
- 教育技术的教育技术
- 计算机科学 计算机科学
- 软件工程教育 软件工程教育
背景情况:
- 协作学习很普遍,但预测学生表现仍然具有挑战性.
- 当前的方法在协作活动中经常忽略空间,时间和文本数据.
- 软件工程项目为研究团队动态提供了丰富的环境.
研究的目的:
- 提出一种新的框架,GOAT,用于加强协作学习中的学生绩效建模.
- 将空间,时间和文本特征纳入现有方法经常错过的内容.
- 提高软件工程团队项目中预测学生表现的准确性.
主要方法:
- 开发了全球本地优化图形变压器 (GOAT) 框架.
- 构建了动态知识概念-增强的交互图.
- 集成的空间意识和时间意识模块用于动态交互建模.
- 利用全球-本地优化模块来分析团队内部和团队间的关系.
主要成果:
- GOAT有效地模拟了随着时间的推移在学习团队内和跨学习团队之间的动态交互.
- 该框架捕捉了复杂的关系,突出了团队成员的共同点和差异.
- 在真实数据集上的实验验证证明了GOAT在现有方法上的优越性.
结论:
- 拟议的GOAT框架在模拟和预测协作软件工程项目的学生表现方面取得了重大进展.
- 整合不同的数据特征 (空间,时间,文本) 会导致更准确的性能预测.
- GOAT提供了一个强大的方法来分析复杂的协作学习动态.
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