Evaluation of awarding badges on Student’s engagement in Gamified e-learning systems
Bibliographic record
Abstract
Abstract Gamification has been gaining increasing acceptability in recent times in educational and commercially related activities, as a tool that encourages and improves the motivation of digital native learners. Since learners can easily engage, educationists have explored gamification as a tool for remediation of engagement, motivation, and collaboration. However, the literature showed that the structural and contextual deployment of game elements is defined only partially in practice. Subsequently, gamification success and failure factors should be explored to identify the required enhancement to achieve improved efficiency in current systems. This research extracts the relevant aspects of gamification that need due consideration to make a guided choice through existing theories. This study is based on an online gamified study that uses well-founded concepts in teaching and evaluation of students in a university. Although badges earned and time spent indicated an increase in engagement, the results show that further work needs to be done by incorporating feedback elements, social interaction, and interactive guidance. The underlying impression is that timely, frequent feedback and personalized guidance, avenues for collaboration and interactivity need to be explored towards the better utility of gamification. Therefore, learning culture in the current learner-centered environment should be further studied to infuse better productivity.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".