Socially Shared Regulation and Performance in Group Work on Creativity Tasks: Analyzing Regulation Utterances
Bibliographic record
Abstract
This study examined socially shared regulation of learning (SSRL) and motivation processes in a collaborative learning task that required creativity using the ICT tool of mind mapping. Thirty university students formed three groups, collaborating face-to-face to generate creative ideas. The following results were obtained from qualitative and quantitative data using psychological scales and utterance analysis. In the middle phase of the collaborative activity, there was a significant weak-to-moderate positive correlation between socially shared regulation of cognition, self-regulation, co-regulation, and socially shared regulation of intrinsic motivation and a deep level of regulation utterances. Moreover, there were significant weak-to-moderate correlations between behavioral and cognitive engagement, SSRL of monitoring and cognition, and the three modes of motivational regulation. Creative performance was significantly and moderately positively associated with socially shared regulation of cognition and total frequency of utterances in the group. Based on these findings, the implications for practice in university education are discussed from the perspective of socially shared regulation in collaborative learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".