Team-Based Learning in professional writing courses for accounting graduates: positive impacts on student engagement, accountability and satisfaction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".