Factors Associated with Failure in Accounting: A Case Study of the Omani Students Rodrigo M. Velasco1
Bibliographic record
Abstract
Anywhere in the world, accounting is highly regarded as one of the most challenging subjects in business programs. This is usually associated with a high failure rate; a pressing issue that deserves an intervention. This paper analyzed the factors associated with failure in accounting as experienced by a college in the Sultanate of Oman in two-folds: teachers' and students' perspectives. The sequential explanatory mixed method of research was utilized through the quantitative survey and focus group discussion (FGD) supplemented by an interview. Based on the students' assessment of the challenges and study habits, the teachers' attribution of failure to students' skills and capabilities is affirmed. It is very much recommended that teachers go back to the basic reinforcement of skills-building strategies to help the students pass the module. Since this is a prelude to a higher analysis, further studies of the same nature are highly encouraged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".