Insights into teaching a complex skill: Threshold concepts and troublesome knowledge in electroencephalography (EEG)
Bibliographic record
Abstract
Background: Threshold concepts (TCs) are defined as ideas within a discipline that are often conceptually difficult (“troublesome”), but when learned, transform a learner’s understanding. Electroencephalography (EEG) has been recognized as a conceptually difficult field in neurology, and a study of threshold concepts in EEG may provide insights into how it is taught and learned. Methods: Semi-structured interviews were performed with 12 EEG experts in the US and Canada. Experts identified potential TCs and troublesome knowledge, and explored how these concepts were taught and learned. Interview transcripts were coded and analyzed using a general thematic analysis approach, based on the core elements of the threshold concepts framework. Results: One concept (polarity) emerged most clearly as a threshold concept. Other troublesome areas included pattern interpretation and clinical significance, but these lacked some of the characteristics of TCs. Several themes emerged, including the role of TCs and troublesome knowledge in determining expertise and the role of prior experience. Conclusions: We have used the threshold concepts framework to explore potential barriers to learning, suggest ways to support learners, and identify potential points of emphasis for teaching and learning EEG. A similar approach could be applied to the study of teaching and learning in other conceptually difficult areas of medical 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".