Emergent Explicit Regulation in Collaborative College Science Classrooms
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».