An Exploratory Case Study of the Implications of Gamification Theory's Impact on Adult Learners in Post-Secondary Computer Science Classes
Bibliographic record
Abstract
This exploratory case study examines the impact of gamification theory on adult learners in computer science classes. This is an empirical study of how effectively adult learners retain and learn through the use of gamification and innovative educational techniques in the classroom. This is studied from the online and traditional classroom approaches. At this time, adult learners were more receptive to learning programming through entertaining and enticing puzzles and games in traditional and online classrooms. Their retention of knowledge gained from these classes was evaluated by using problem-solving puzzle assignments and in-class gamification results. The online and traditional classroom data were compared and contrasted to determine the effectiveness of gamification theory and educational techniques in the classroom by incorporating evidence from tests, assignments, and in-class gamification contests. This study demonstrates that students learn and retain more material on computer science topics when gamification theory introduces games, assigned work transformed into practical puzzles requiring technical problem-solving skills using software tools, and other educational techniques in the classroom. It argues for the use of gamification theory as a means to engage multi-generational students in learning complex and advanced material in a fun and involved educational setting. This can occur in both traditional and online classroom conference tools such as; Zoom, or Microsoft Teams.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".