Promoting Inclusive Accounting Education through the Integration of Stem Principles for a Diverse Classroom
Bibliographic record
Abstract
In recent times, accounting education has explored many innovative approaches to address the collective needs of students. Integrating STEM (Science, Technology, Engineering, and Mathematics) principles in accounting education promises an increased potential for technology-driven solutions, enhanced learning strategies, and inclusivity. This study investigated the effects of integrating STEM principles into accounting education. The specific objective was to understand the impact of STEM integration on student engagement, academic performance, diversity, and inclusivity within accounting programs. The study adopts quantitative methods, using surveys to collect primary data from 342 respondents ranging from lecturers, teachers, and students, while Linear regression was used to test the hypotheses. The study revealed a positive relationship between STEM integration and student academic performance and engagement. Results also showed that integrating STEM principles into accounting education positively impacted inclusivity and attracted a diverse classroom. The study recommends that accounting education policymakers develop a curriculum that aptly incorporates STEM concepts into accounting courses that would appeal to students from diverse backgrounds; and the accounting faculty of tertiary institutions should play a vital role in STEM integration and inclusivity by undertaking training sessions on STEM-related skills, inclusive teaching methods, and cultural sensitivity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".