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Record W2293414049

Research results for Mecanika: a game to learn Newtonian concepts

2011· article· en· W2293414049 on OpenAlexaff
François Boucher-Genesse, Martin Riopel, Patrice Potvin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMathematics educationGame mechanicsTest (biology)Physics educationPsychologyComputer scienceMultimedia
DOInot available

Abstract

fetched live from OpenAlex

A large body of research in mechanics indicates that interactive engagement teaching methods usually have higher chances of influencing students' conceptions than direct instruction. A few researchers specifically studied the impact of videogames on Newtonian Physics instruction through empirical means, with some limited success. Mecanika is a free online game that sets itself apart from previous work by simply offering puzzling physics situations, without attempting to explain the theory in the game. Students who used the game as homework, facilitated with classroom debriefings and guidebooks, wielded significantly higher gain than a control group on the standard Force Concept Inventory test. Students who only played as homework registered a similar gain, even though Mecanika was never mentioned the classroom. This gain was unexpected, since the game does not make any physics concept explicit, and was designed to be integrated in a classroom setting.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.259
GPT teacher head0.493
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations9
Published2011
Admission routes1
Has abstractyes

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