An Analysis of Gamification and Game-Based Learning as Strategies for Anti-Oppressive Education
Bibliographic record
Abstract
Educational institutions have historically been environments where oppression takes place in the forms of racism, sexism, ableism, homophobia, transphobia, and classism among others (Kumashiro, 2000; Chen-Hayes, 2001; Dedotsi & Paraskevopoulou-Kollia, 2019). Anti-oppressive education is the active rejection of or refusal to participate in forms of oppression that take place in schools, and in turn facilitating strategies for education that works against oppression (Kumashiro, 2000). There are existing theories for how to promote and engage this anti-oppressive education, such as introducing narratives and education about marginalized communities that counter and challenge educators’ preconceived biases about students (Warren, 2023; Kumashiro, 2000), transforming schools into safe and welcoming spaces that provide students with support, advocacy, and resources specific to their identities, and through acknowledgement and embracing of their complex and unique identities (Kumashiro, 2000). Gamification and game-based learning are emerging as new teaching practices in classrooms and have benefits in several areas such as lesson engagement, learning outcomes, classroom environment, accessibility practices, collaboration in the classroom, teaching delivery, learning effectiveness, exploration and risk-taking in a safe environment, and the student’s sense of control, agency, and ownership over their learning process. However, there is a gap in the educational research literature on the use of gamification and game-based learning as potential strategies for combating the various forms of oppression that take place in schools. They have not yet been thoroughly explored for their potential to be beneficial for anti-oppressive education. This study explores how gamification and game-based learning can be tools to promote education that supports students in classrooms, creates excitement around learning, and contributes to an anti-oppressive learning environment through providing education about and for marginalized groups, counter-narratives that combat some educators’ prejudiced beliefs about equity-deserving students, and providing education that has the power to change society through challenging both implicit and explicit social and cultural biases as well as building empathy and a deeper understanding of some of the lived experiences of marginalized communities. This analysis is driven by close readings of two digital games— Lucas Pope’s Papers, Please (2013) and McKinney’s SPENT (2011)—, an in-depth discussion of theories of oppression and anti-oppression, and an analysis of publicly available policy documents from eleven of Ontario’s public school boards, universities, and colleges, including: Waterloo Region District School Board, Toronto District School Board, York District School Board, Thames Valley District School Board, Wilfrid Laurier University, University of Guelph, University of Waterloo, Toronto Metropolitan University, Conestoga College, Mohawk College, and Fanshawe College.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".