Mapping as a learning strategy in health professions education: a critical analysis
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
CONTEXT: Mapping is a means of representing knowledge in a visual network and is becoming more commonly used as a learning strategy in medical education. The assumption driving the development and use of concept mapping is that it supports and furthers meaningful learning. OBJECTIVES: The goal of this paper was to examine the effectiveness of concept mapping as a learning strategy in health professions education. METHODS: The authors conducted a critical analysis of recent literature on the use of concept mapping as a learning strategy in the area of health professions education. RESULTS: Among the 65 studies identified, 63% were classified as empirical work, the majority (76%) of which used pre-experimental designs. Only 24% of empirical studies assessed the impact of mapping on meaningful learning. Results of the analysis do not support the hypothesis that mapping per se furthers and supports meaningful learning, memorisation or factual recall. When documented improvements in learning were found, they often occurred when mapping was used in concert with other strategies, such as collaborative learning or instructor modelling, scaffolding and feedback. CONCLUSIONS: Current empirical research on mapping as a learning strategy presents methodological shortcomings that limit its internal and external validity. The results of our analysis indicate that mapping strategies that make use of feedback and scaffolding have beneficial effects on learning. Accordingly, we see a need to expand the process of reflection on the characteristics of representational guidance as it is provided by mapping techniques and tools based on field of knowledge, instructional objectives, and the characteristics of learners in health professions 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.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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