Game-Based Learning: A Scoping Review of Research in Higher Education
Bibliographic record
Abstract
In higher education, game-based learning (GBL) has been implemented across disciplines to enhance learning and to foster peer-to-peer connections (Jääskä & Aaltonen 2022). In the Canadian context, GBL has been embedded into curricular, co-curricular, and research initiatives, but challenges exist in GBL promotion, department affiliation, and staffing (Garbati & Skrepnek 2024). To advocate for GBL, we turn to the literature in learner motivation where scholars have noted GBL as a pillar of differentiated pedagogy (Tsami 2022) as well as a mechanism of learner motivation (Eltahir et al. 2021). While literature intersecting GBL and learner motivation exists, a systematic review of this literature does not. As such, we conducted a scoping review of the literature intersecting GBL and learner motivation in higher education. In educational research, scoping reviews synthesize existing evidence (Gómez Suárez, M. & Jesús Yagüe 2021) and are useful in determining the “scope" and volume of literature on a topic (Munn et al. 2018). To conduct this scoping review, we first searched our university library database (the largest in Canada) and other major databases (e.g., Web of Science, ERIC) using “game-based learning,” “motivation,” and “higher education” as key words. Inclusion criteria included: English peer reviewed journal articles published between January 1, 2020, and January 1, 2025. Our search yielded 166 results. Second, we systematically reviewed each article to identify number of authors, university affiliation, study type (e.g., qualitative, quantitative), context (curricular, co-curricular or research), research fields, program level(s), game genre (e.g., digital, analog), theoretical foundations, and learner motivation categories. Findings point to a positive connection between GBL and motivation as well as enhanced motivation and academic performance through digital GBL. Authorship composition trends toward multiple authors. This scoping review provides a comprehensive understanding of the landscape of GBL as a motivating factor in higher education, particularly in relation to student-centered initiatives, differentiated assessment strategies, and study tools. Given the current and increasing interest in GBL as both a differentiated pedagogical tool and a mechanism for fostering student motivation, this review provides a foundation for future research and offers insights for educators, scholars, and institutional leaders seeking to implement or expand GBL initiatives within higher education.
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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 | 0.031 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".