Using epistemic network analysis and self-reported reflections to explore students' metacognition differences in collaborative learning.

Metacognition is important in self-regulated learning and understanding its epistemic network can improve teaching and learning. We collected self-reported metacognition reflections on collaborative learning activities from 87 college students to analyze how students' metacognitive patterns differ by performance level and discipline type. We used an epistemic network analysis to identify these differences, and the results indicated that description of goals appeared most in self-reported reflections. There are variations in metacognitive patterns between different groups. High-score students had stronger connections around actions, while low-score students had stronger connections between metacognitive knowledge and context. The natural science students focused more on metacognitive knowledge and actions, while the humanities science students focused more on metacognitive experience and context. This implies that teachers should provide clear explanations about the collaborative learning goal, and a group strategy that takes both performance and discipline types into consideration could address the variation in metacognitive patterns. • Students' metacognitive patterns were investigated through self-report reflections. • Epistemic network analysis was used to compare the metacognition differences between different groups. • Students use most of their energy on thinking about learning goals in metacognitive process. • Different groups (performance, discipline) have different metacognitive patterns in collaborative learning. [ABSTRACT FROM AUTHOR]