Engaging Engineering Education: A Gamification-Based Learning Approach
Bibliographic record
Abstract
This work-in-progress research-to-practice paper introduces the Gamified Learning Puzzle (GLP) platform to enhance student engagement and learning effectiveness in engineering education through gamification. The GLP platform design is tailored towards the sequential and prerequisite-based nature of engineering courses whilst being extendable to other disciplines as well. The platform is developed as a web-based application, removing the need for learners to be in a specific location or use a particular device. The back-end development utilizes the Django framework, chosen for its feature-rich environment, and MySQL for robust database management. Key gamification elements such as “Points”, “Levels/Iteration”, and “Feedback” are integrated into the design to enhance the learning experience. The platform consists of two main components, the Instructor Interface and the Learner Interface. The Instructor Interface allows the course instructor to populate a question bank, organize these questions by topic, and create levels within each topic that increase in difficulty. It also enables the specification of prerequisite relationships between levels. The Learner Interface includes an uncompleted jigsaw puzzle that reveals its pieces as learners progress through the game. Preliminary trials will involve the integration of the platform into select engineering courses at a pilot level, refining the plat-form based on feedback obtained from focus groups. To measure impact on students' learning experience and learning outcome, comprehensive surveys and study of pre- and post-exposure grades will be conducted. Further plans include integrating AI algorithms to provide learners with personalized feedback and assist instructors in generating content.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".