A Systematic Literature Review on Critical Thinking in an Undergraduate Accounting Programme
Bibliographic record
Abstract
The ability of accountancy students to apply critical thinking in solving problems related to modules offered in accountancy undergraduate programme, has remained a concern for many higher institutions. Accountancy students tend to struggle to apply critical thinking to their course work offered in the programme. To be able to apply critical thinking, accountancy students in undergraduate programmes should be able to apply a series of attributes that can enable them to understand questions asked in case study questions or multiple tasks. This study reviewed various ways in which students can apply critical thinking in an undergraduate accounting programme. A systematic literature review was conducted to examine the challenges undergraduate accounting students in South Africa face in applying critical thinking. This study found some of the barriers for applying critical thinking include redundancy, backwardness, application, extent to which preferences are associated to learning, and the ability to engage in real life accountancy practice. Literature reveals that accounting students can use the cube method which includes six steps: describe, compare, associate, analyse, apply, and argue to address the challenges of applying critical thinking. The study however, proposed some strategies that academics can use to ensure that accountancy students are able to apply critical thinking in solving accounting problems. Students are able to apply critical thinking in problem solving. Suggestions for future research were provided.
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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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".