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Enregistrement W2075384036 · doi:10.1002/bmb.2003.494031040269

Response to “the problem with problem‐based medical education: Promises not kept” by R. H. Glew

2003· article· en· W2075384036 sur OpenAlexaboutno aff
Howard S. Barrows

Notice bibliographique

RevueBiochemistry and Molecular Biology Education · 2003
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueProblem and Project Based Learning
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCurriculumAssertionProblem-based learningComputer scienceVariety (cybernetics)Taxonomy (biology)Mathematics educationArtificial intelligencePsychologyPedagogy

Résumé

récupéré en direct d'OpenAlex

Glew's thoughtful paper [1] addresses important concerns about the quality of problem-based learning curricula. His assertion is that “poor implementation of problem-based curricula has grossly compromised its effectiveness and kept it from reaching its potential.” Although this is an increasing concern now, it has been present for over two decades. To address this concern at that time I proposed a taxonomy for the wide variety of educational methods that were being referred to as problem-based learning [2]. The intent of the taxonomy was to allow teachers to recognize the variant they were using and to understand what of the educational goals possible with problem-based learning the variation they were using did and, more importantly, did not address. By doing this it was hoped that teachers would move to a well-designed problem-based learning curriculum that would address all the educational objectives possible with problem-based learning to provide their students with the full advantages of the method. The term problem-based learning became increasingly popular, and it almost seemed as though many schools spent more time advertising their use of the method in presentations and brochures than learning the knowledge and skills needed to mount such an approach. So many poorly conceived problem-based learning approaches are now in existence that the taxonomy is inadequate. Teachers who might be interested in learning about the method witnessing one of these approaches could easily decide that problem-based learning offers little advantage over what they already do and is not worth the effort to adopt the method. There is the real risk that this trivialization of problem-based learning will lead to its demise, and students will be all the poorer. In desperation, I changed the name of well-designed problem-based learning to “authentic problem-based learning” [3] to allow it to be distinguished from poorly designed or poorly carried out approaches. The use of “authentic” has two useful meanings. The first is that it represents the true or authentic problem-based learning method that evolved over the past 30 years through continual research and development. The second is the educational meaning of authentic as it refers to methods that require the learner to use the skills valued in the real world after graduation as they learn. Such a move only serves to signal what is well-designed problem-based learning and perhaps sets a standard for the method. But it doesn't help correct the problems already out there. Well-designed, or authentic, problem-based learning has now been around long enough for a number of comparative curricular evaluations to be carried out showing that students learn as much science, perform better clinically, and continue to educate themselves more effectively in the years after graduation [4–6]. With authentic problem-based learning, the promise has been kept. Although most comparative evaluations use instruments that assess outcomes addressed by conventional lecture-based curricula when comparing them to problem-based learning, it is hoped that future comparisons will assess performance in line with the unique educational objectives of problem-based learning, as does the study of Shin et al. [6]. Glew describes a number of reasons for the existence of these problems with problem-based curricula at the student, faculty, and administrator level. However, I would argue that the basic reason for these problems is that most faculty with significant responsibility for teaching have never been educated in education and prepared for that responsibility. Depending on their role in the school, faculty members are well prepared for research and patient care responsibility, but not for the responsibility of educating medical students. Problem-based learning is based on important principles of the learning sciences and requires a sophistication beyond knowing how to dispense the facts of a specialty or discipline in lectures, writing written test questions, and running demonstrations, labs, and seminars. Educated curriculum unit designers will ensure a well-designed and -evaluated problem-based curriculum. A well-trained problem-based learning facilitator (or tutor) could correct many of the problems described by Glew as they appear in the small learning group. When the medical school at Sherbrooke University in Quebec, Canada undertook significant educational changes including problem-based learning, a carefully designed, well-received, and successful, ongoing program of faculty educational development in preparation for the changes was undertaken [7]. Just as one of the founders of the specialty of medical education in this country, Stephen Abrahamson, said at the opening of a workshop I attended as a young neurologist in his first academic appointment, “Just because you are an expert -ologist of some kind or other it doesn't mean you know how to teach it.” That had a profound effect on me then. I think it is even more important now. Requiring an education in education for all faculty with significant educational responsibility is not only logical but responsible, and with the brains and talent of medical school faculty could lead to all kinds of valuable educational developments.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,052
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,050
Score d'incertitude au seuil0,068

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,052
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,007
Communication savante0,0050,009
Science ouverte0,0060,006
Intégrité de la recherche0,0500,083
Charge utile insuffisante (le modèle a refusé de juger)0,0200,010

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.

Tête enseignante Opus0,007
Tête enseignante GPT0,308
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2003
Routes d'admission1
Résumé présentoui

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