How does the Type of Task Influence the Performance and Social Regulation of Collaborative Learning?
Bibliographic record
Abstract
In this paper we analyze the effects of the type of collaborative task (elaboration of concept map vs elaboration of expository summary) on the performance and on the level of collaboration achieved by Mexican university students in the multimedia learning of a social sciences content (Communication Psychology). Likewise, the processes of social regulation that are put into play in these collaborative tasks are described. Forty-five students (17 women and 28 men) grouped in 15 triads participated in the study. Each triad was assigned to one of the two collaborative conditions: elaboration of concept map (8 groups) and elaboration of an expository summary (7 groups). It was monitored that there were no significant previous differences between two conditions regarding: reading comprehension, reading comprehension regulation strategies and domain-specific prior knowledge. To evaluate the performance in learning, the quality of the proposals made in concept maps and summaries were taken adapting the procedure proposed by Haugwitz, Nesbit and Sandmann (2010), and also the results obtained by the students in a multiple-choice questionnaire about the knowledge area. Likewise, the level of collaboration perceived by each member of the teams was examined using a Collaboration questionnaire developed by Chan and Chan (2011). The identification and characterization of the processes of social regulation was carried out through a qualitative analysis of the exchanges registered during the collaborative activity, considering the type (co-regulation and shared regulation) and the regulation orientation (directed to the task or to the management of collaboration). The quantitative results analysis showed the existence of significant effects working with collaborative concept maps in the knowledge acquired during the collaborative task and in some of the indicators of perceived collaboration. Although no significant statistical differences were found, in the teams that elaborated expository summaries, a predominance of episodes of regulation directed towards the cognitive activity of the collaborative task was observed, being scarce, in both conditions, the episodes of social regulation directed towards collaboration within the triads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".