A Playful Approach to Fostering Motivation in a Distance Education Computer Programming Course: Behaviour Change and Student Perceptions
Bibliographic record
Abstract
The central role of motivation to learn in distance education has been noted, and gamification has been proposed as one approach to promote student motivation. This study explores promoting motivation in a distance education, third-year computer programming course via a gamified approach to improve coursework participation and student experience. Motivation was examined from a Self-Determination Theory (SDT) perspective, as gamified approaches often rely on external motivation and the explicit use of competition to engender internal motivation leading to desired behaviours. The results of using gamification in education are mixed, and its use is controversial. Two cycles of action research on the introduction of eight playful elements are reported on, and data relating to student engagement with the course and a student questionnaire was gathered. There was little evidence that the intervention led to behaviour change or improved scores; however, students responded very positively to the intervention, although some negative themes emerged. The extent to which the playful approach supported the basic psychological needs of SDT is discussed and the intervention’s results critically considered, including whether the effort involved in such an approach was worth it. It was concluded that such playful approaches might have positive motivational effects.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".