Abstract
Identifying the effects of students’ collaborative regulation behavior when working on a task is an important step towards a better understanding of how collaboration supports learning. We discuss a study where we combined analysis of students’ dialogues with an automated analysis of their action patterns as they constructed science models in an open-ended learning environment. Our results show that students use different types of collaborative regulation be-haviors, and that these behaviors affect their performance on the system as well as their pre-post learning gains. We also showed that groups, which adopt more shared regulation used different learning strategies than groups that did not.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2017 Computer-Supported Collaborative Learning Conference |
| Publisher | Lawrence Erlbaum Associates |
| Pages | 319-326 |
| Number of pages | 8 |
| Publication status | Published - 22-Jun-2017 |
| Externally published | Yes |
Keywords
- METACOGNITION
- Collaborative learning