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Record W1729718587

USING VIDEO GAMES IN COMPUTER SCIENCEEDUCATION

2014· article· en· W1729718587 on OpenAlex

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

VenueEuropean Scientific Journal ESJ · 2014
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceMultimediaVideo gameEmpirical researchTurns, rounds and time-keeping systems in gamesGame mechanicsMathematics educationVideo game designPsychology
DOInot available

Abstract

fetched live from OpenAlex

Recent research on the use of video games for computer science (CS) education was reviewed to identify potential benefits, summarize useful experiences, synthesize empirical evidence on the effectiveness of video game use for CS education, and identify areas for further research. The benefit most frequently-identified was increased student motivation, particular to learn programming. Implementation strategies include using games to motivate students, making games to teach CS topics, and using games as environments or examples to teach CS topics. Methodologies vary widely and results about the effectiveness of these uses are inconclusive. Further empirical research is needed to better understand the potential impact and effective education implementation of video games in computer science.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.069
GPT teacher head0.355
Teacher spread0.286 · 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