Gamifying History: Designing and Implementing a Game-Based Learning Course Design Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper analyzes the development and implementation of a game-based learning course design framework. Drawing inspiration from task-based learning, the framework is structured around four core gamified elements: narrative assignment design; learner discovery; team-based collaboration and competition; and choice through quests. The intended goal of implementing this framework is to improve learner engagement and foster greater learner investment in the course. The framework, developed at the University of Waterloo, was integrated into the course design for—and subsequently taught in—a third-year history course. A mixed-methods analysis was conducted in which students (n = 15) were surveyed, interviewed, and observed throughout the course at different intervals. The results of the study suggest that the team-based nature of the framework and the embedded gameplay elements are most effective at improving engagement for learners, while some form of extrinsic motivation is still beneficial to ensure all learners find completing additional tasks worthwhile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it