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计算机科学学生在混合课程设计中的自我调节学习的自我报告和数字追踪措施

Feifei Han¹, Robert A Ellis²

¹Institute for Learning Sciences and Teacher Education, Australian Catholic University, Brisbane, QLD Australia.

Education and information technologies

|June 26, 2023

概括

在混合课程中学生自我调节学习的自我报告和数字追踪措施显示出有限的对齐. 了解这些差异可以帮助教育工作者支持学生.

科学领域:

教育技术的教育技术
学习科学学习科学
高等教育高等教育

背景情况:

混合学习环境要求学生有效地自我调节他们的学习.
通过自我报告和数字痕迹评估自我调节学习 (SRL) 提供了互补的见解.
了解这些措施之间的协调对于有效的干预设计至关重要.

研究的目的:

在混合计算机科学课程中调查SRL的自我报告和数字追踪测量之间的对齐.
为了比较被确定为更好/更差的自我调节学习者的学生的特征,与更多/不那么活跃的在线学习者相比.
探索SRL测量和学业成绩之间的关系.

主要方法:

利用来自激励学习策略问卷 (MSLQ) 的自我报告数据和在线学习活动互动的数字痕迹数据.
采用分层集群分析来根据自我报告和数字追踪数据对学生进行分类.
进行单向ANOVA和交叉表格,以分析集群之间的差异和关联.

主要成果:

与较差的自我调节学习者相比,更好的自我调节学习者在特定的在线活动中表现出更高的交互频率.
更活跃的在线学习者报告了更高的自我效能,内在动机和SRL策略的使用.
在自我报告和数字追踪学生群体之间发现了显著但较弱的关联.

结论:

混合学习中SRL的自我报告和数字追踪措施提供了部分一致的,但却截然不同的观点.
干预措施应考虑两种类型的数据,以全面理解和支持学生SRL.
同行学习,即熟练的自我调节学习者分享策略,可能会在混合环境中有利于扎的学生.

关键词:

混合课程设计的设计.计算机科学学生计算机科学学生数字追踪措施是指数字追踪措施.自主调节的学习学习自我报告的措施

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