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Record W3170765071 · doi:10.21432/cjlt27990

Teaching with Sandbox Games: Minecraft, Game-Based Learning, and 21st Century Competencies

2021· article· en· W3170765071 on OpenAlex
Cristyne Hébert, Jennifer Jenson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of British ColumbiaUniversity of Regina
Fundersnot available
KeywordsSandbox (software development)Creativity21st century skillsMathematics educationPedagogyPsychologyExploratory researchVideo game developmentGame based learningGame designComputer scienceSociologyMultimedia

Abstract

fetched live from OpenAlex

In this paper, we present the findings of a research study, working with 12 educators in a large urban school board in Ontario using Minecraft for 21st century competency development. We identify a number of pedagogical moves teachers made to support 21st century learning through communication and collaboration, both in the classroom and in the game world, and three approaches to play, directed/guided, scaffolded, and open, that represented a three tiers of critical thinking and creativity/innovation. We argue that while an open, exploratory sandbox game such as Minecraft can meaningfully aid students in the development of 21st century competencies, it is in fact teachers’ decisions around how the game will be used in the classroom that determine whether or not 21st century competency development is supported.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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