Emergent Explicit Regulation in Collaborative College Science Classrooms
Bibliographic record
Abstract
Abstract This paper is a work in progress paper. Small-group activities have been adopted widely in science and engineering classrooms. Research on collaborative learning has showed that some groups are more productive (in terms of meeting the learning goals of the program) than others, even if all the groups are taught by the same instructor, and are engaging in the same tasks. Group regulation is defined as groups adaptively responding to challenges in order to optimize group learning. However, empirical studies on group regulation are scarce. This study is intended to explore how college science students regulate their groups during collaborative learning. In our video data, first year college students are engaged in small group scientific inquiry and engineering design activities. The activities are also developed to promote students' group collaboration and reflective thinking. Activities of different types —such as reading and discussing scientific articles, designing and conducting experiments, model building— were analyzed qualitatively. First we broadly explored the videos, and noticed self-initiated group regulations emerging in different kinds of activities across different groups. We then conducted thematic coding identifying typical examples of the group regulation phenomena and examining them more closely. In previous work we had developed a set of contextual and behavioural features of the observed group regulation and named the phenomena emergent explicit group regulation (EER). We understand EER as an "in the moment," or emergent, regulatory response to a challenge faced by the group. In this work, we further refine this framework by applying it to additional data. In line with the literature on regulation, we focus on instances where a group was faced with decision making in order to be able to move their work forward. Further, there was significant tension in the group on which path to take to move forward. The conditions, context and specific characteristics of each instance of EER have similarities and variation, which can relate to the effect on the group's productivity. Not all EER are equally productive. Primarily, EER features that can be demonstrated in activities include: timing of demonstrated EER, the person who takes the initiative and regulates the group, regulative discourse, actions taken to regulate, physical positioning change when EER emerges, context in which an emergent EER would be helpful or necessary, and the direction the EER leads to. By analyzing more data, we anticipate generating a more robust framework that characterizes both the challenge encountered as well as the regulatory response that was produced. With this in hand we wish to analyze patterns in the data to better understand when EER leads to productive or unproductive outcomes. Then, based on this information, generate implications for learners and their instructors that will help stimulate more productive group interactions in scientific inquiry and engineering design.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".