Mapping Metacognition: Uncovering Strategic Knowledge in Action
Bibliographic record
Abstract
While concept mapping is widely recognized as an effective active learning strategy, it is still underutilized in higher education. The current literature in SoTL demonstrates the utility of concept maps but the means by which the learning benefits are realized are not well explored. This study focuses on two first year anatomy and physiology courses where nursing students utilized pre-structured concept maps, called skeleton maps, as an assigned weekly study strategy. In this research, we analyzed the skeleton maps collected from participating students for evidence of metacognitive strategies in use. We found five main approaches: the use of diagrams and drawings, color, highlighting, sticky notes (layering), and cross-referencing. Together, these approaches demonstrate elaboration and organizational knowledge, both components of metacognitive strategic knowledge that are at a greater level of sophistication than basic rehearsal. The strategies used by students also demonstrated aspects of the self-knowledge component of metacognition.Rehearsal was the main strategy that students reported pursuing (and the strategy observed by the instructor) prior to the pedagogical change. Overall, our results show that the skeleton maps significantly shifted the students’ approaches to learning and encouraged metacognitive approaches previously shown to enhance recall and organization of knowledge. This study provides insight into how skeleton maps can support higher level metacognitive learning strategies, providing evidence to encourage their introduction in content-heavy courses.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".