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Record W2076860243 · doi:10.2190/ec.44.4.c

Enhancing Learning through an Online Secondary School Educational Game

2011· article· en· W2076860243 on OpenAlexafffundabout
David Kaufman, Louise Sauvé, Lise Rénaud

Bibliographic record

VenueJournal of Educational Computing Research · 2011
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversité du Québec à MontréalUniversité TÉLUQSimon Fraser University
FundersSimon Fraser University
KeywordsEducational gameGame based learningMathematics educationVariety (cybernetics)Sample (material)PsychologyCognitionMedical educationProtocol (science)Computer scienceMultimediaMedicine

Abstract

fetched live from OpenAlex

This article consists of four sections: (1) the problems associated with asthma in the province of Québec and across Canada; (2) the theoretical framework for the learning enhanced by our online educational game entitled Asthme: 1,2,3 … Respirez! ( Asthma: 1,2,3 … Breath!), created by adapting the popular board game Parcheesi, and intended for students in a senior secondary school health education program (14 to 18 years old); (3) the methods employed, including a description of the educational game, the quasi-experimental research protocol, the sample, the variables being studied, the measurement instruments, the pilot study, the procedures, and the data analysis; and (4) the results of the study and discussion of the results. The results of the paired t-tests showed significant improvements in a variety of cognitive skills after students played the game on laptops in their classrooms for 40–60 minutes. No differences were found between males and females. These results are encouraging for teachers who wish to use educational digital games in their 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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.209
GPT teacher head0.483
Teacher spread0.274 · 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

Citations11
Published2011
Admission routes3
Has abstractyes

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