Using cueing from question pairs to engage students in reflective thinking: An exploratory study
Bibliographic record
Abstract
In this exploratory study, guided by dual process theories of reasoning, we used a low-stakes diagnostic test in a large introductory calculus-based physics course to test the effectiveness of using multiple-choice question pairs to improve student performance on conceptual multiple-choice questions.As part of this study, we measured students' tendency to engage analytic reasoning via the Cognitive Reflection Test, a three-item questionnaire embedded in a start-of-term diagnostic.These pairs of questions used a common question stem to ask about different but related concepts that students often conflate, such as acceleration and force in the context of a collision.Focusing on three questions from previously piloted question pairs, and controlling for measures of student knowledge and tendency to engage analytic reasoning, we used mixed-effects logistic regression techniques to observe that students who received the question as part of a pair were 7.2 times (95% confidence interval [4.8, 10.9], p < .001)more likely to answer the question correctly relative to having the question alone.Furthermore, the intervention was more impactful for students with a lower tendency to engage analytic reasoning.These results have implications for the design of short-answer physics questions in learning and assessment situations.
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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 | 0.039 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".