Profiling of Learners in Medical Schools as a Move Toward Precision Education: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Academic experiences seek to get the best out of learners, maximizing performance and developing the skills and competencies needed to foster lifelong learning. The more personalized and tailored the academic experience among learners, the better the outcome. Precision education is a novel approach to research and practice, which is concerned with identifying and tailoring education to the precise needs of the learner. An emerging area of precision education is using data to develop learner profiles for a better understanding of individual learners relative to the characteristics and competencies of lifelong learners. OBJECTIVE: This scoping review aims to identify literature that reports on profiling learners within medical schools. Our review, as described in this paper, will describe the characteristics being measured, the methods and data sources used to generate profiles, and the resulting profiles that emerge. This review aims to provide guidance to those supporting medical school learners on the current state of learner profiling. METHODS: This scoping review will use the Population, Concept, and Context framework, published by Joanna Briggs Institute, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. The search strategy was developed in collaboration with a library specialist. An initial search was conducted in PubMed, ERIC, Google Scholar, Cochrane, CINAHL, and SCOPUS. Data will be extracted, and 2 authors will undertake the screening procedure using the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews checklist. RESULTS: The database searches yielded 166 results, and title and abstract screening of 135 extracted articles is currently underway after eliminating 31 duplicates. We anticipate the scoping review to be completed in the first week of October 2022. The final scoping review will present the findings in a narrative and pictorial fashion. CONCLUSIONS: This review will help guide scholars looking to understand the current state of learner profiling within medical schools. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/41828.
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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 | 0.122 | 0.125 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.017 |
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".