Using the Threshold Concept Framework to Explore Student Learning by PBL in Two UK Medical Schools
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
Problem-based learning (PBL) is resource-intensive, but promotes skills like uncertainty tolerance, and knowledge application (e.g. (1)). During curriculum review at two UK Medical Schools, we explored Year 1 medical undergraduates’ learning in PBL. \n \nThreshold Concepts (TCs) are widely-studied across disciplines, less-so in Medicine (2). TCs are integrative, irreversible, and troublesome (3). They differ from ‘core concepts’ in being crucial for subject mastery, and transformative (“change in knowing, doing, being, and future learning”) (4). Without them, students can get stuck in a ‘liminal’ state of oscillating or incomplete understanding, experiencing uncertainty and discomfort, mimicking understanding (6). \n \nPBL may enable students to learn Troublesome Knowledge (TK, (5)) and TCs, in several ways (7, 8). PBL itself may constitute a ‘Threshold Capability’ (7). We therefore explored PBL learning in relation to the TC and TK models, and asked what promotes or hinders learning. We compared results between two Schools, to identify commonalities, and explore differences that may arise from variations how PBL is enacted. \n \nStudents (n= 19) recorded instances during PBL that meet criteria based on the TC definition (3,9) (e.g. struggles, frustration, coming together, seeing things differently, aha moments) To avoid bias, TCs were not mentioned. Tutors (n=12) also recorded any such instances. ‘Straight-after-the-moment’ audio-diaries minimised hindsight bias (10). Data analysis was as in Neve et al (7): focused on the TC Framework; but allowing for emergent coding categories and alternative theories. A priori codes included each TC criterion (3,9), enablers and barriers to learning; emergent codes included aspects of PBL and subject areas. Codes and themes were compared between Schools. \n \nAt both sites, students identified learning instances that involved characteristics of TCs and/or TKs, at multiple stages of PBL. Both content knowledge and PBL skills were identified. Tutor accounts provided confirmation and additional insights. However, the number of areas identified and extent to which they met the TC definition, differed between Schools. \n \nWe describe learning areas identified; and discuss implications for SoTL in three areas: Medical Education, Threshold Concepts, and PBL. We give evidence of how PBL can help students learn troublesome and threshold concepts, and of methods can enable and hinder this; and discuss methodological limitations of our study. We consider the role of PBL in undergraduate medical curricula at both Schools, and beyond. Finally, we demonstrate the utility and limitations of the TC framework, adding to critical debate (4) about the definition and identification of TCs. \n \n(1) Koh, G.C.H., Khoo, H.E., Wong, M.L., & Koh, D. (2008). The effects of problem-based learning during medical school on physician competency: A systematic review. Canadian Medical Association Journal, 178(1), 34-41. \n \n(2) Neve H, Wearn A, Collett T. 2016. What are threshold concepts and how can they inform medical education? Medical Teacher 38(8): 850-853. \n \n(3) Meyer JHF & Land R. (2003). ‘Threshold Concepts and Troublesome Knowledge (1) – Linkages to Ways of Thinking and Practising’ In: Improving Student Learning – Ten Years On. C.Rust (Ed), OCSLD, Oxford. \n \n(4) Barradell S, Peseta T. Promise and challenge of identifying threshold concepts: a cautionary account of using transactional curriculum inquiry. Journal of Further and Higher Education 2016; 40(2):262–75.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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