Leveling Up Learning: Game Based Learning Initiatives in Canadian Higher Education
Bibliographic record
Abstract
Game based learning (GBL), the use of game design elements within non-game contexts such as education, became increasingly popular in the early 2000s, yet empirical evidence about the benefit of games on student learning remains inconclusive. The potential impact of GBL can be influenced, for example, by design elements, context, and discipline. In Canadian higher education, GBL may be housed within departments, the library, or centres for teaching and learning. At our institution, GBL initiatives have only recently begun to surface; for example, the Department of English and Drama has recently launched a games studies minor for undergraduate students, the library has an extensive collection of video games available to borrow, and the academic skills centre has a large collection of board games that are used primarily for social game cafés. While opportunities for curricular connections may exist, our academic skills centre currently lacks staff who have both capacity and expertise in GBL pedagogy. Exploring possibilities for expansion, we aimed to understand the landscape of GBL initiatives across Canadian post-secondary institutions. To achieve this aim, we conducted an environmental scan of over 100 Canadian post-secondary institutions, gathering data such as the existence of GBL programs, category and level of programming (curricular, co-curricular, or research), initiative types, research development, faculty involvement, and availability of additional resources. Findings indicate inconsistencies in definitions used to promote GBL and a concentration of GBL initiatives at the curricular and research levels. The majority of GBL in Canada is led by faculty through course and degree-level programs at both undergraduate and graduate levels. This research gives us insight into GBL program development, challenges, and opportunities for higher education in Canada and globally.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".