Next Steps in the Implementation of Learning Analytics in Medical Education: Consensus From an International Cohort of Medical Educators
Bibliographic record
Abstract
BACKGROUND: With the implementation of competency-based assessment systems, education programs are collecting increasing amounts of data about medical learners. However, learning analytics are rarely employed to use this data to improve medical education. OBJECTIVE: We identified outstanding issues that are limiting the effective adoption of learning analytics in medical education. METHODS: Participants at an international summit on learning analytics in medical education generated key questions that need to be addressed to move the field forward. Small groups formulated questions related to data stewardship, learner perspectives, and program perspectives. Three investigators conducted an inductive qualitative content analysis on the participant questions, coding the data by consensus and organizing it into themes. One investigator used the themes to formulate representative questions that were refined by the other investigators. RESULTS: Sixty-seven participants from 6 countries submitted 195 questions. From them, we identified 3 major themes: implementation challenges (related to changing current practices to collect data and utilize learning analytics); data (related to data collection, security, governance, access, and analysis); and outcomes (related to the use of learning analytics for assessing learners and faculty as well as evaluating programs and systems). We present the representative questions and their implications. CONCLUSIONS: Our analysis highlights themes regarding implementation, data management, and outcomes related to the use of learning analytics in medical education. These results can be used as a framework to guide stakeholder education, research, and policy development that delineates the benefits and challenges of using learning analytics in medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.131 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".