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Record W4238910775 · doi:10.32920/ryerson.14649459

Building blocks: Children's open-ended play in Minecraft

2021· preprint· en· W4238910775 on OpenAlexaff
Liam O’Donnell

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentUniversity of Toronto
Fundersnot available
KeywordsStrict constructionismConstructionismAutonomyFocus groupPedagogyQualitative researchSociologyPsychologyMathematics educationEpistemologyPolitical scienceDevelopmental psychologySocial science

Abstract

fetched live from OpenAlex

This qualitative research study tracks the evolution of the video game Minecraft as a tool for education and connects it with constructionist theories of learning. It highlights an emerging model of pedagogy that severs these constructionist connections in favour of heteronomous, teacher-directed lessons that limit children’s autonomy and negate their affinity with cultures surrounding Minecraft. To explore alternatives to this teaching model, eight children, aged 6 to 8 years, engaged in open-ended, self-directed play in Minecraft over four 20-minute sessions and shared their experiences through focus group discussions. The results highlight the importance for educators to create a constructionist culture around Minecraft by allowing children to autonomously pursue their intrinsic interests and respecting their affinity with practices not traditionally welcomed in the classroom, including playing with in-game explosives. The paper concludes with guidelines for teachers to implement these practices and create a constructionist culture in their own classrooms.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.010
Scholarly communication0.0050.004
Open science0.0020.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.361
Teacher spread0.327 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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