Gamification in Higher Education Promotion: The Development and Implementation of TIC ACC TOE and e-TACC-TOE for ACCA Programme Recruitment
Bibliographic record
Abstract
Ineffective promotional activities, such as traditional slide presentations, often fail to capture students' attention, resulting in low awareness and reduced enrollment. Developing an interactive promotional game presents a more engaging solution to boost audience engagement and assess knowledge retention. TIC ACC TOE and e-TACC-TOE were developed as gamification tools to improve promotional activities for the ACCA programme at DPAS, UiTM Shah Alam. Initially a physical board game, TIC ACC TOE was later digitised into e-TACC-TOE to adapt to online promotional needs, particularly during the COVID-19 pandemic. These tools effectively engage students and enhance knowledge retention, leading to a noticeable increase in applications for the ACCA programme. With both physical and digital versions, the tools support diverse promotional activities, helping UiTM contribute to Malaysia’s national goal of producing 60,000 chartered accountants by 2030. Other institutions can adapt this product innovation for various activities, including promotions, student inductions, and industrial talks, and have commercial potential as a mobile app, improving accessibility and overcoming device limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".