Developing the role of big data and analytics in health professional education
Bibliographic record
Abstract
As we capture more and more data about learners, their learning, and the organization of their learning, our ability to identify emerging patterns and to extract meaning grows exponentially. The insights gained from the analyses of these large amounts of data are only helpful to the extent that they can be the basis for positive action such as knowledge discovery, improved capacity for prediction, and anomaly detection. Big Data involves the aggregation and melding of large and heterogeneous datasets while education analytics involves looking for patterns in educational practice or performance in single or aggregate datasets. Although it seems likely that the use of education analytics and Big Data techniques will have a transformative impact on health professional education, there is much yet to be done before they can become part of mainstream health professional education practice. If health professional education is to be accountable for its programs run and are developed, then health professional educators will need to be ready to deal with the complex and compelling dynamics of analytics and Big Data. This article provides an overview of these emerging techniques in the context of health professional education.
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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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.013 |
| 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".