An Analysis of Gamification and Game-Based Learning as Strategies for Anti-Oppressive Education
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».