Suitability of Multiple-choice Questions in Evaluating the Objectives of Academic Educational Process of Accounting Specialization
Bibliographic record
Abstract
This study attempt to investigate and explore the use of multiple-choice questions (MCQs) at Jordanian public and private universities, from the point view of faculty member who are teaching accounting specialization and use MCQs in examinations of accountancy subjects. The study seeks to identify the extent MCQs can achieve regarding level of knowledge among students, assess the deep understanding and In-depth learning to measure and evaluate the range of learning in subjects of accounting specialization. To achieve the objectives of the study, a questionnaire designed according to the requirements of Ministry of Education in Jordan and the International Accounting Education Standard Board (IAESB).Based on analyzing responses and examining hypotheses, results clarify that MCQs are not enough tools to achieve the educational objectives concerning accounting subjects, in both theoretically and practically aspects. It was clear that there are no significant differences between the opinions of faculty members, neither public nor private universities. The study recommends that there is a need to re-consider the use of MCQs as a tool to evaluate the students’ abilities and skills in subjects of accounting specialization.
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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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".