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Record W7054633926

An Analysis of Gamification and Game-Based Learning as Strategies for Anti-Oppressive Education

2023· article· en· W7054633926 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsOppressionAcknowledgementNarrativeTransformative learningDiversity (politics)Class (philosophy)Active learning (machine learning)
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.014
GPT teacher head0.276
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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