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Record W2171112642 · doi:10.1177/1046878108325451

Validity of a Simulation Game as a Method for History Teaching

2008· article· en· W2171112642 on OpenAlexaff
Pierre Corbeil, Dany Laveault

Bibliographic record

VenueSimulation & Gaming · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTest (biology)ComprehensionMathematics educationPsychologyGame based learningComputer science

Abstract

fetched live from OpenAlex

The aim of this research is, first, to determine the validity of a simulation game as a method of teaching and an instrument for the development of reasoning and, second, to study the relationship between learning and students’ behavior toward games. The participants were college students in a History of International Relations course, with two groups participating in a simulation game and one serving as a control group. The results show that the experimental groups had higher scores on a test of comprehension and that participants within these two groups who were identified as operating at a level of formal reasoning (as evaluated by an arrangement test) obtained the highest knowledge scores. Learning style was found to be an important variable in explaining motivation toward the game.

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)0.047
metaresearch head score (Gemma)0.233
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.233
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.364
GPT teacher head0.509
Teacher spread0.146 · 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

Citations41
Published2008
Admission routes1
Has abstractyes

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