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Record W2162337951 · doi:10.3109/0142159x.2011.558948

A 3-year experience implementing blended TBL: Active instructional methods can shift student attitudes to learning

2011· article· en· W2162337951 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Teacher · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedical educationContext (archaeology)Maturity (psychological)Team-based learningPsychologyInstructional designActive learning (machine learning)Blended learningMathematics educationComputer scienceMedicineEducational technology

Abstract

fetched live from OpenAlex

Medical educators have been encouraged to adopt active instructional strategies that require learners to engage in and direct their own learning. These innovations may be seen as disruptive and face early challenges due to student resistance. We report 3 years of experience implementing a blend of team-based learning (TBL) and online learning modules in an undergraduate medical course. Three sequential cohorts of first year medical students were surveyed exploring how they valued different instructional methods during a period of evolving curricular design. In addition to a demonstrated increase in acceptance of new teaching methods, there was a shift in student perceptions of the relative merits of didactic, online and TBL teaching. Medical students' appreciations of different instructional methods are influenced by the maturity of instructional design. Educational change is best viewed through a longer term lens, acknowledging the necessity for teachers to develop experience in implementing new methods in the context of their institution.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.000

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.

Opus teacher head0.081
GPT teacher head0.452
Teacher spread0.371 · 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