ODL Study Skills: Evidence from Professional Accounting Qualification Students
Bibliographic record
Abstract
Online distance learning (ODL) is known to have its challenges, especially for students pursuing a professional accounting qualification course that has a high failure rate. This is due to its wide and in-depth syllabus coverage and higher order thinking examination demands that cause great challenges to the students. Thus, determining the best study skills for students pursuing this qualification is paramount so that they can complete the course within the programme duration despite the additional burden of ODL. This study aims to determine the best study skills set that could enhance students' ability to pass their professional accounting examination. This study employs a quantitative approach and collects data using a questionnaire survey. The respondents are students pursuing the professional accountancy programme, Association of Chartered Certified Accountants (ACCA) offered at University Teknologi MARA (UiTM). The results reveal that two of the most dominant study skills are organizing and processing information, followed by study aids and note-taking. The findings from this study provide input to learning providers and professional bodies in curating materials and support programs that could further enhance students' chances of passing the examination.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".