Finding traces of self-regulated learning in activity streams.

LAK(2018)

引用 71|浏览50
暂无评分
摘要
This paper aims to identify self-regulation strategies from studentsu0027 interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.
更多
查看译文
关键词
Self regulation, Learning strategies, Blended-learning, Clickstream activity, Learning analytics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要