Impact of accounting software utilization on students' knowledge acquisition
Bibliographic record
Abstract
Purpose – This study investigates the impact that software utilization may have on students' knowledge acquisition of the accounting cycle. Differences in knowledge acquisition are examined between three groups of students: those who completed an accounting case manually using the traditional pencil and paper approach, using software, and first manually and then using software. The main research question is: “To what extent does using computers to study the accounting cycle lead to better knowledge acquisition?” This paper aims to inform changes in accounting education. Design/methodology/approach – The survey method was employed to collect information from accounting students in a Canadian business school. A total of 1,053 usable questionnaires were returned. Declarative knowledge and procedural knowledge are the theoretical underpinnings. Findings – The results indicate that students who first completed the case manually and then completed the same case using accounting software experienced the best knowledge acquisition. This suggests that the best manner for students to acquire concrete knowledge of the accounting cycle is by completing cases using both methods. The results also indicate that students who completed the case using only the software experienced better knowledge acquisition than did students who completed the case only manually. This suggests that software can be effectively utilized and integrated in class to improve knowledge acquisition of accounting information systems. Originality/value – Little investigation has been performed on the usefulness and impact accounting software utilization may have on students' level of learning. The findings may benefit students and faculty members by helping in curriculum design changes, course design, and computer implementation decisions. The findings of this study have the potential to make a difference in the way that educators teach and business students learn. Business education may be improved by the judicious use of software in the classroom.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".