From Distraction to Contribution: A Preliminary Study on How Peers Outside the Group Can Contribute to Students’ Learning
Bibliographic record
Abstract
Active Learning Classrooms are new learning spaces that allow collaborative learning activities to take place easily over the traditional classroom. However, some features of these rooms could be viewed as “distracting” to students’ learning such as the multiple interactive screens. The purpose of this paper is to begin the conversation on how subtle roles in the learning environment could impact learning. Using a case study approach, an activity from one course was chosen that exemplified how peers outside students’ immediate group can influence their learning. Based on the preliminary findings, it is suggested that being aware of these subtle roles peers outside the group can have on students and making them explicit in the pedagogical design of the course can lead to maximizing the usage of the space to potentially foster greater learning. Les salles de classe où l’on pratique l’apprentissage actif sont de nouveaux espaces d’apprentissage qui permettent d’organiser des activités d’apprentissage collaboratif plutôt que de pratiquer l’enseignement traditionnel. Toutefois, certains aspects de ces salles de classe peuvent être considérés comme « gênants » pour l’apprentissage des étudiants, par exemple les multiples écrans interactifs. L’objectif de cette communication est d’ouvrir le débat sur la manière dont les rôles subtils de l’environnement d’apprentissage peuvent avoir des effets sur l’apprentissage. En utilisant l’approche qui consiste à faire une étude de cas, une activité d’un cours donné a été choisie pour exemplifier comment les pairs qui se trouvent à l’extérieur du groupe immédiat des étudiants peuvent influencer leur apprentissage. Selon les résultats préliminaires, il semblerait que le fait d’être conscient de ces rôles subtils que les pairs qui se trouvent à l’extérieur du groupe peuvent avoir sur les étudiants et le fait de les rendre explicites dans la conception pédagogique du cours peuvent mener à maximiser l’usage de l’espace en vue de favoriser un meilleur apprentissage.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".