三元化多式联络数据:在计算机辅助的语言学习环境中,交互日志的数据,学习成绩和L2学习者的动机
Mihwa Lee1,2, Björn Rudzewitz1,2, Yushan Ye3
1Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Walter-Simon-Straße 12, Tübingen, 72072, Germany.
Data in brief
|January 21, 2026
概括
这项研究分析了来自智能计算机辅助语言学习 (ICALL) 系统的第二语言 (L2) 英语阅读数据. 研究结果揭示了学习者的动机和数字行为如何预测L2阅读成就.
科学领域:
- 教育技术的教育技术
- 获得第二语言的学习.
- 应用语言学 应用语言学
背景情况:
- 智能计算机辅助语言学习 (ICALL) 系统产生了丰富的交互数据.
- 了解学习者参与度及其与熟练程度的关系对于优化L2学习至关重要.
- 多模式数据,包括交互日志和熟练度得分,为学习过程提供了全面的见解.
研究的目的:
- 分析201名学习者使用ICALL系统进行L2英语阅读的详细交互日志数据.
- 研究学习者行为,动机和L2阅读能力之间的关系.
- 探索数字痕迹对学习成绩的预测能力.
主要方法:
- 在六周内从学习者那里收集了116,168个点击流数据点.
- 在干预前和后进行的L2阅读能力测试.
- 收集的自我报告的动机措施.
- 分析多式联运数据,整合交互日志,测试成绩和动机.
主要成果:
- 确定了不同的学习者导航模式和任务参与行为.
- 证明了特定的数字阅读行为与L2熟练程度的提高之间的相关性.
- 展示了动机如何调节数字痕迹和学习成果之间的关系.
结论:
- 学习者与ICALL系统的接触为L2阅读发展提供了宝贵的见解.
- 数字痕迹可以有效地预测L2阅读中的学习成绩.
- 整合学习者层面的因素,如动机,可以提高对ICALL有效性的理解.
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