Utilising Online Gamification to Promote Student Success and Retention in Tertiary Settings
Bibliographic record
Abstract
The role of gamification in Australian higher educational learning has gained increasing currency in recent years, with many proponents promoting its usefulness for improving the university student experience by increasing progression and lowering attrition, particularly among first year students (Charles, Charles, McNeill, Bustard, & Black, 2011). However, some students express reservations that the inherently competitive nature of some gamified learning activities negatively impact their learning experience, especially when compared to classic instructional methods (Charles et al., 2011). This discussion and instructional paper undertakes a review of the gamification literature within the Australian higher education context, concurrently exploring what it means and how to use gamification to enhance student learning. The paper provides a short biographic summary of the positive impact selected popular gamified activities has had on improving student engagement, participation and retention in tertiary settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".