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Record W2884563407

Communication in problem based learning

2018· dissertation· en· W2884563407 on OpenAlexaboutno aff
Pauline Bryant

Bibliographic record

VenueUEA Digital Repository (University of East Anglia) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsBrainstormingProblem-based learningConversationEnthusiasmMathematics educationPsychologyPedagogyComputer scienceArtificial intelligenceCommunicationSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Problem Based Learning (PBL) is one of the many ways that undergraduates are supported to learn in Norwich Medical School. PBL is an instructional design model that was first introduced into medical schools in Canada in the 1960s. Theoretical arguments were put forward in the medical education literature that claimed PBL was a revolutionary, new and superior learning method. The method attracted considerable enthusiasm, but it also attracted controversy. Descriptions of the process were diverse and the pedagogy was illusive. Never the less, it subsequently spread worldwide, and thousands of medical students now learn in PBL groups. The purpose of my research was to explore PBL at Norwich medical school and to find ways to improve it. My focus was on communication in PBL tutorials, and the aim was to identify communicative elements that realised and hindered effective dialogue. The objectives were to 1) Consider the theoretical framework of PBL and explore enablers and barriers to effective dialogue, 2) Consider the learning environment of the PBL tutorial, particularly brainstorming, and identify ways in which to maximise the learning opportunities, 3) Determine how this knowledge can be used to facilitate effective dialogue to take place between learners in PBL. The main research question was; What communicative strategies can be used by tutors to enhance elaborative dialogue to take place in brainstorming in Problem Based Learning tutorials? Using elements of Conversation Analysis (CA), I explored communication in PBL. Focusing particularly on brainstorming, I identified specific communicative elements that were used by tutors to facilitate elaborative dialogue. Elaborative dialogue, in which students explain their thinking, appears to be of particular importance in the learning and understanding of concepts. Elaboration includes; a) Verbalising conceptual understanding, b) Identifying conflicting information, c) Co-construction of understanding, d) Answering/ asking relevant questions, It is clear that the extent to which students benefit from working in small groups depends on the quality of interaction between students within the group. I identified communicative elements that were inhibitory to elaborative dialogue. In addition, I identified that there were contextual factors that inhibited communication between students in the tutorials for example, students were expected to chair tutorials, but they struggled to perform the role. The findings from my study are useful for PBL tutors who can use these elements of conversation analysis to examine their own practices; for example, tutors can audio record a section of a PBL tutorial, and identify their own questioning techniques that have promoted useful dialogue between learners, and reflect on their practice. This conversation analysis method can be applied to other small group teaching methods in other disciplines and by other organisations. I hope this will serve as a starting point to encourage individual tutors and institutions to explore ways to enhance communication in PBL tutorial groups and enrich the learning experiences for students.

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)0.005
metaresearch head score (Gemma)0.018
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.012
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0140.002

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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