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Record W4391562907 · doi:10.18260/1-2--41060

Emergent Explicit Regulation in Collaborative College Science Classrooms

2024· article· en· W4391562907 on OpenAlexaff
Pierre-Philippe A. Ouimet, Ying Cao, Tong Wan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.414
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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