Exploring Serious Play in the Engineering Classroom to Support the "Joyification" of Education and Learning
Bibliographic record
Abstract
Serious play has the potential to enable creative and advanced learning experiences while incorporating enjoyable/fun learning activities. This can be highly relevant in engineering education, where the gravity and responsibility associated with the profession can be daunting for both students and educators. This study explores the integration of serious play in the software engineering classroom. The exploration aimed to transform classroom dynamics and find more effective ways to connect students with the gravity of the engineering discipline while enhancing student satisfaction and understanding during the learning process and adding fun elements. Two distinct serious play activities were designed and evaluated. The first was inspired by Stanford's D.School's Gift Giving Project and the Lego Serious Play activity, while the second drew inspiration from popular board games like Monopoly and Trivial Pursuit. Both serious play activities were evaluated in early-year undergraduate and graduate software engineering classrooms, where qualitative feedback was gathered to measure student perception toward the joyification of their education and learning. This paper describes the designed serious play activities, discusses student evaluation results, and discusses opportunities for continual improvement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".