Discussion Group Effectiveness is Related to Critical Thinking through Interest and Engagement
Bibliographic record
Abstract
Higher student enrolment at North American tertiary institutions over the last decade has led to a greater reliance on lecturing in large classes (i.e., 50 students or more). The efficiency of lecturing as a method of instruction can sometimes come at the cost of student interaction, engagement, critical thinking and satisfaction. Implementing discussion groups in large lecture classes is one technique that can reverse these costs, however it is not clear what it is about discussion groups that promotes these outcomes. Drawing on social identity theory (Tajfel, 1981), the present study examined the roles of two discussion group characteristics: identification and effectiveness, in predicting course interest and engagement, critical thinking and application, and course satisfaction among psychology students assigned to discussion groups in a large class ( N = 81) over a 12-week period. Findings indicated that discussion group effectiveness, but not discussion group identification, predicted course interest and engagement, critical thinking and application, and course satisfaction. Importantly, the relationships between discussion group effectiveness and critical thinking and application, and discussion group effectiveness and course satisfaction were mediated by course interest and engagement. When discussion groups help students to understand and engage with new ideas and information, this can promote the interest and engagement that can promote positive outcomes. The implications of these findings are discussed.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".