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Record 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 on OpenAlexaboutno aff
Howard S. Barrows

Bibliographic record

VenueBiochemistry and Molecular Biology Education · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumAssertionProblem-based learningComputer scienceVariety (cybernetics)Taxonomy (biology)Mathematics educationArtificial intelligencePsychologyPedagogy

Abstract

fetched live from 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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.050
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0050.009
Open science0.0060.006
Research integrity0.0500.083
Insufficient payload (model declined to judge)0.0200.010

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.

Opus teacher head0.007
GPT teacher head0.308
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations17
Published2003
Admission routes1
Has abstractyes

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