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Record W3151167864 · doi:10.1080/09639284.2021.1906720

Team-Based Learning in professional writing courses for accounting graduates: positive impacts on student engagement, accountability and satisfaction

2021· article· en· W3151167864 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

VenueAccounting Education · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsMcGill University
Fundersnot available
KeywordsAccountabilityTeamworkTeam-based learningPsychologyNegotiationCurriculumMedical educationPedagogyStudent engagementSociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The accounting curriculum has been criticised for failing to develop accounting students’ professional and generic skills for the future needs of employers. This paper describes a constructivist active learning approach, namely Team-Based Learning (TBL), to embed professional skills in a postgraduate professional writing course for accountants. The study explores TBL as an effective team pedagogy that enhances student engagement, accountability and satisfaction. Using a mixed-method approach to analyse student scores on individual and team Readiness Assurance Tests, responses to the TBL Student Assessment Instrument, mid-semester survey and end-of-semester team peer assessment, the study found the TBL method developed teamwork, communication, negotiation and problem-solving skills, and individual and team accountability. Since these skills are critical for successful accounting careers, the findings provide empirical grounding for adopting TBL methodology in accounting communication courses and could form the basis for implementing the methodology in other business and management disciplines.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.332
Teacher spread0.310 · 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