Constructivist Meta-practices: When Students Design Activities, Lead Others, and Assess Peers
Bibliographic record
Abstract
New educators may feel overwhelmed by the options available for engaging students through classroom participation. However, it may be helpful to recognize that participatory pedagogical systems often have constructivist roots. Adopting a constructivist perspective, our paper considers three meta-practices that encourage student participation: designing activities, leading others, and assessing peers. We explored the consequences of these meta-practices for important student outcomes, including content knowledge, engagement, self-efficacy, sense of community, and self-awareness. We found that different meta-practices were associated with different combinations of outcomes. This discovery demonstrates the benefit of studying meta- practices so as to reveal the nuanced effects that may arise from pedagogical choices. In addition, an understanding of meta-practices can help leadership educators to be more discerning and intentional in their course designs.
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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 | 0.102 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".