Teaching and Learning Gaps in Accounting Education: Implications for the Employability of Accounting Graduates
Bibliographic record
Abstract
There is a substantial body of empirical literature on university students' self‐perceived approaches to learning, but evidence on instructors' perceptions of the way they facilitate their students' learning approaches is less evident. This study aims to investigate the extent of the gap between students' learning approaches and instructors' teaching orientations towards facilitating these approaches. The subsequent employability of accounting graduates depends in part on the nature and extent of this gap. Student learning approaches are measured on two dimensions ‐ deep and strategic approaches ‐ drawn from Tait's and Entwistle's (1995) Revised Approaches to Studying Inventory (RASI). Instructors' facilitation of students' learning is measured by a re‐orientation of the same RASI instrument towards teaching approaches. The results reveal several significant differences of emphasis between instructors and students in terms of deep and strategic approaches. Students are falling short of what their instructors believe they are facilitating in terms of the development of their employability competencies and characteristics for a professional career. When students are grouped according to gender, further significant differences are found. Implications of these findings for future change in accounting education are discussed.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".