Epistemic climate and epistemic change: Instruction designed to change students' beliefs and learning strategies and improve achievement.
Bibliographic record
Abstract
The purpose of this study was to assess the effectiveness of an intervention designed to foster epistemic change over the course of 1 semester. The intervention was based on constructivist teaching practices that incorporated teacher modeling of critical thinking of content, evaluation of multiple approaches to solving problems, and making connections to prior knowledge. Sixty-three students across 2 classrooms (one intervention [n = 31], one control [n = 32]) participated and completed questionnaires 5 times over the semester. Questionnaires measured students' epistemic beliefs, learning strategies, and levels of motivation for their statistics class. Results revealed that for students in the intervention class, their epistemic beliefs shifted midway through the semester, whereas students in the control group maintained a consistent level of beliefs throughout the semester. Intervention students' self-reported use of critical thinking and elaboration strategies also significantly increased midway through the semester, as did their levels of self-efficacy for learning statistics. In contrast, students in the control group maintained a consistent level of strategy use and self-efficacy. Finally, students in the intervention group had a significantly higher final grade compared with those in the control group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".