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Difficult incidents and tutor interventions in problem‐based learning tutorials

2009· article· en· W1953554400 on OpenAlexafffund
Pawel M. Kindler, Christopher P. Grant, Steven Kulla, Gary Poole, William Godolphin

Bibliographic record

VenueMedical Education · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsUniversity of British Columbia
FundersDivision of Undergraduate EducationFaculty of Medicine, University of British Columbia
KeywordsPsychological interventionTUTORMedical educationPsychologyCurriculumProblem-based learningSet (abstract data type)Dysfunctional familyMedicineMathematics educationPedagogyComputer scienceClinical psychology

Abstract

fetched live from OpenAlex

CONTEXT: Tutors report difficult incidents and distressing conflicts that adversely affect learning in their problem-based learning (PBL) groups. Faculty development (training) and peer support should help them to manage this. Yet our understanding of these problems and how to deal with them often seems inadequate to help tutors. OBJECTIVES: The aim of this study was to categorise difficult incidents and the interventions that skilled tutors used in response, and to determine the effectiveness of those responses. METHODS: Thirty experienced and highly rated tutors in our Year 1 and 2 medical curriculum took part in semi-structured interviews to: identify and describe difficult incidents; describe how they responded, and assess the success of each response. Recorded and transcribed data were analysed thematically to develop typologies of difficult incidents and interventions and compare reported success or failure. RESULTS: The 94 reported difficult incidents belonged to the broad categories 'individual student' or 'group dynamics'. Tutors described 142 interventions in response to these difficult incidents, categorised as: (i) tutor intervenes during tutorial; (ii) tutor gives feedback outside tutorial, or (iii) student or group intervenes. Incidents in the 'individual student' category were addressed relatively unsuccessfully (effective < 50% of the time) by response (i), but with moderate success by response (ii) and successfully (> 75% of the time) by response (iii). None of the interventions worked well when used in response to problems related to 'group dynamics'. Overall, 59% of the difficult incidents were dealt with successfully. CONCLUSIONS: Dysfunctional PBL groups can be highly challenging, even for experienced and skilled tutors. Within-tutorial feedback, the treatment that tutors are most frequently advised to apply, was often not effective. Our study suggests that the collective responsibility of the group, rather than of the tutor, to deal with these difficulties should be emphasised.

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)0.008
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.063
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.386
Teacher spread0.366 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations24
Published2009
Admission routes2
Has abstractyes

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