Monitoring and regulation of learning in medical education: the need for predictive cues
Bibliographic record
Abstract
CONTEXT: Being able to accurately monitor learning activities is a key element in self-regulated learning in all settings, including medical schools. Yet students' ability to monitor their progress is often limited, leading to inefficient use of study time. Interventions that improve the accuracy of students' monitoring can optimise self-regulated learning, leading to higher achievement. This paper reviews findings from cognitive psychology and explores potential applications in medical education, as well as areas for future research. COGNITIVE PSYCHOLOGY: Effective monitoring depends on students' ability to generate information ('cues') that accurately reflects their knowledge and skills. The ability of these 'cues' to predict achievement is referred to as 'cue diagnosticity'. Interventions that improve the ability of students to elicit predictive cues typically fall into two categories: (i) self-generation of cues and (ii) generation of cues that is delayed after self-study. Providing feedback and support is useful when cues are predictive but may be too complex to be readily used. APPLICATION TO MEDICAL EDUCATION: Limited evidence exists about interventions to improve the accuracy of self-monitoring among medical students or trainees. Developing interventions that foster use of predictive cues can enhance the accuracy of self-monitoring, thereby improving self-study and clinical reasoning. First, insight should be gained into the characteristics of predictive cues used by medical students and trainees. Next, predictive cue prompts should be designed and tested to improve monitoring and regulation of learning. Finally, the use of predictive cues should be explored in relation to teaching and learning clinical reasoning. CONCLUSIONS: Improving self-regulated learning is important to help medical students and trainees efficiently acquire knowledge and skills necessary for clinical practice. Interventions that help students generate and use predictive cues hold the promise of improved self-regulated learning and achievement. This framework is applicable to learning in several areas, including the development of clinical reasoning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".