An Empirical Investigation of the Different Levels of Gamification in an Introductory Programming Course
Bibliographic record
Abstract
Adding gaming elements to conventional teaching methodologies has gained a lot of attention because of its ability to incorporate an engaging, motivating, and fun-based environment. As a result, learners' dedication and performance are also better. Unfortunately, current gamification models do not consider the effect of different levels of gamification. Therefore, this study provides deeper insight into the three levels of gamification on the motivation, engagement, and performance of 450 undergraduates enrolled in an online course. The level of gamification is experimentally manipulated based on different gaming elements and the presentation of learning content. The outcomes were measured at three points. Quantitative methods were used to analyze defined measures, and qualitative methods were used to analyze open-ended measures. The results revealed no change in outcomes between all groups during pre-course and mid-course assessments. However, motivation, engagement, and performance are improved in gamified environments, and these effects are more noticeable towards the end of the course. It was discovered that the gamification level was a significant determinant of motivation and performance but not engagement, which highlights the importance of implementing gamification in educational platforms. The gamification appeared to be a pedagogically profound way of engaging students in the online course. The whole setup triggered the learner’s motivation to learn and perform in the course. We conclude that gamification does help in motivation, engagement and performance if considered properly. Thus, educators and educational institutions seeking to enhance student motivation and performance may look at the ‘right level’ of gamification as an appropriate methodology.
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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) | 0.000 | 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) | 0.002 | 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".