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Record W2186427145 · doi:10.11645/9.2.2029

Can playing Minecraft improve teenagers’ information literacy?

2015· article· en· W2186427145 on OpenAlex
Sandra Bebbington, André Vellino

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.

Bibliographic record

VenueJournal of Information Literacy · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of OttawaBishop's University
Fundersnot available
KeywordsRelevance (law)Information literacyRanking (information retrieval)Function (biology)PsychologyLiteracyComputer sciencePedagogyPolitical scienceInformation retrieval

Abstract

fetched live from OpenAlex

Some research suggests that a significant number of Generation Z teenagers (those born in the late 1990s or early 2000s) display an insufficient level of information literacy (IL) to function effectively in an information-based society. Yet many of them are gamers who succeed at accomplishing game-related tasks that require a number of IL skills such as information seeking, the critical assessment of sources and relevance ranking of information. This paper describes the results of an interpretive case study of the information behaviours of teenage gamers that supports the hypothesis that the online game Minecraft supports the development of such IL skills. The online interactions of 510 participants of a public discussion forum on Minecraft and interviews from eight teenage Minecraft gamers, as well as the game itself, were analysed. This study suggest that some aspects of Minecraft’s design effectively induce players to seek out game-related information in affinity spaces (online informal learning spaces), select appropriate sources, evaluate the information shared by fellow gamers and decide which information best satisfies their needs.

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)0.001
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 score0.814
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.011
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.318
Teacher spread0.302 · 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