The Dynamics Between Self-Regulated Learning and Learning Outcomes: an Exploratory Approach and Implications

METACOGNITION AND LEARNING(2022)

引用 11|浏览2
暂无评分
摘要
Self-regulated learning (SRL) has been linked to improved learning and corresponding learning outcomes. However, there is a need for more precise insights into how SRL during learning contributes to specific learning outcomes. We operationalised four learning outcomes that varied on two dimensions: structure/connectedness and level/deepness of knowledge. Specifically, we assessed how surface knowledge measured with a domain knowledge test (independent concepts) and a concept map (connected concepts), and deep knowledge measured with a transfer test (independent concepts) and an essay (connected concepts) were associated with frequencies of SRL activities during learning, assessed by concurrent think aloud, while taking into account students’ metacognitive and prior knowledge. Forty-four university students performed a 45-minute problem-solving task integrating information about three topics to write a vision essay on the future of education. Results of the pre-/post-test analysis revealed a learning gain in domain knowledge and concept maps. Low cognitive activities were associated with all knowledge measures, except the concept maps and transfer. Furthermore, specific low cognitive activities showed either a positive or negative association; for example, processing showed a positive association with essay, but a negative association with domain knowledge. High cognitive activities were associated with the essay (connected concepts), but not with the concept map. Both metacognitive activities and knowledge were related to transfer. To conclude, taking the level and structure of knowledge into account helps specify the association between SRL activities during learning and the related learning outcomes.
更多
查看译文
关键词
Self-regulated learning, Learning outcomes, Metacognition, High cognition, Deep strategies, Low cognition, Surface strategies
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