Attention spreads between students in a learning environment.
Bibliographic record
Abstract
defined as the spread of attentive (or inattentive) states among members of a group. We examined attention contagion in a learning environment in which pairs of undergraduate students watched a lecture video. Each pair consisted of a participant and a confederate trained to exhibit attentive behaviors (e.g., leaning forward) or inattentive behaviors (e.g., slouching). In Experiment 1, confederates sat in front of participants and could be seen. Relative to participants who watched the lecture with an inattentive confederate, participants with an attentive confederate: (a) self-reported higher levels of attentiveness, (b) behaved more attentively (e.g., took more notes), and (c) had better memory for lecture content. In Experiment 2, confederates sat behind participants. Despite confederates not being visible, participants were still aware of whether confederates were acting attentively or inattentively, and participants were still susceptible to attention contagion. Our findings suggest that distraction is one factor that contributes to the spread of inattentiveness (Experiment 1), but this phenomenon apparently can still occur in the absence of distraction (Experiment 2). We propose an account of how (in)attentiveness spreads across students and discuss practical implications regarding how learning is affected in the classroom. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".