Metacognition in Teaching: Using A “Rapid Responses to Learning” Process to Reflect on and Improve Pedagogy
Bibliographic record
Abstract
In this paper, we critically evaluate the use of a weekly “rapid responses (RR) to learning” process in the context of teaching a graduate course on research methods over a three-year period. The RR process involves use of a short set of open-ended questions about key moments in learning that students complete, in writing, during the last five minutes of each class. The questions ask students to identify salient take-away messages, note when they felt the most and least engaged, name actions taken by anyone that were affirming or confusing, and consider specific “aha” moments. Our specific aim was to assess the following questions: What was the pedagogic value of the RR process? How did it inform our teaching and to what extent were there direct benefits of the process for students as well as for us as teachers? We found that the systematic feedback we obtained in this way supports weekly monitoring of student learning, facilitates response to trouble spots, and assists in assessment of student engagement and classroom climate. It also provides insight into the efficacy of pedagogic strategies, invites students to engage in metacognitive learning about their own learning, and models a process of instructors receiving feedback and being flexible to change. For instructors, the process enhances motivation and professional development and can be used to document instructor leadership and development. Finally, it facilitates deeper appreciation of the need to better integrate student self-assessment and the development of metacognitive skills as core components of the course.
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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 | 0.050 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".