Dynamic Interplay between Modes of Regulation During Motivationally Challenging Episodes in Collaboration
Bibliographic record
Abstract
The cognitive and social demands of collaboration can raise significant motivation challenges. Task progression relies on team members strategically taking control of the problems and adapting accordingly. Theory indicates that productive collaboration involves groups using three modes of regulation: self-regulation, co-regulation, and socially shared regulation. Despite research demonstrating the occurrence of all three modes in collaboration, it is unclear how these modes interact and how co-regulation supports the emergence of self- and shared-regulation of motivation. The study aimed to examine the role co-regulation played in dynamically stimulating the emergence of self- and shared-regulation of motivation. A cross-case comparison was conducted between two groups who experienced high levels of motivation challenges but achieved contrasting perceptions of the overall team learning productivity. During analysis, groups’ dynamic regulatory processes within the online environment were visually represented using a tool called the Chronologically-ordered Representation for Tool-Related Activity (CORDTRA). Findings demonstrate that co-regulation of motivation may afford and thwart the emergence of self- and shared-regulation, and these processes interacted with the group’s situational challenges and the regulatory skills group members possessed. Comparisons between the two groups indicated that groups' motivation regulation should (a) match the demands of the challenges at hand, (b) be positively supported by group members through co-regulation, and (b) involve a more varied strategic responses so that the group may continue to learn and co-construct knowledge effectively as a team.
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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 | 0.002 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".