Learning Curves in Health Professions Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Learning curves, which graphically show the relationship between learning effort and achievement, are common in published education research but are not often used in day-to-day educational activities. The purpose of this article is to describe the generation and analysis of learning curves and their applicability to health professions education. The authors argue that the time is right for a closer look at using learning curves-given their desirable properties-to inform both self-directed instruction by individuals and education management by instructors.A typical learning curve is made up of a measure of learning (y-axis), a measure of effort (x-axis), and a mathematical linking function. At the individual level, learning curves make manifest a single person's progress towards competence including his/her rate of learning, the inflection point where learning becomes more effortful, and the remaining distance to mastery attainment. At the group level, overlaid learning curves show the full variation of a group of learners' paths through a given learning domain. Specifically, they make overt the difference between time-based and competency-based approaches to instruction. Additionally, instructors can use learning curve information to more accurately target educational resources to those who most require them.The learning curve approach requires a fine-grained collection of data that will not be possible in all educational settings; however, the increased use of an assessment paradigm that explicitly includes effort and its link to individual achievement could result in increased learner engagement and more effective instructional design.
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it