Using the Threshold Concept Framework to Explore Student Learning by PBL in Two UK Medical Schools
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».