Bridging Perceptions and Knowledge Acquisition in Accounting: A Comparative Analysis of Learning Methods
Bibliographic record
Abstract
The perception that students have towards accounting contributes significantly to whether they will learn and acquire accounting skills or not. This study investigates the relationship between students' perception of accounting and learning in the presence of a comparative environment involving traditional and blended learning environments. A survey questionnaire was administered to both groups of students in a Greek university and asked them to report demographic information, attitudes toward accounting, and self-assessed ability to perform main accounting functions. To assess learning outcomes, knowledge and skills tests were administered at the beginning and the end of the semester to both the traditional and blended learning groups. The findings show that blended learning students had better scores and perceived accounting as more applicable to their future careers and are more assured that they will be able to apply accounting concepts. They are more assured of performing accounting tasks than students who are not undertaking the blended learning environment. Blended learning also reduces perceived difficulty, which enhances the fun involved in learning. This study contributes to the new emerging literature in accounting education through the illustration of the impact of modes of instruction on students' knowledge acquisition. The findings illustrate that blended learning raises engagement and skill building, which reinforces its increasing adoption in accounting classes.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".