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Record W2115255548 · doi:10.15390/eb.2015.3145

Enhancing Orbital Physics Learning Performance through a Hands-on Kinect Game

2015· article· en· W2115255548 on OpenAlexaff
Maiga Chang, Denis Lachance, Fuhua Lin, Farook Al-Shamali, Nian‐Shing Chen

Bibliographic record

VenueTED EĞİTİM VE BİLİM · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsAthabasca University
Fundersnot available
KeywordsOutreachUsabilityPerceptionAffect (linguistics)AnimationMultimediaPhenomenonVideo gameMathematics educationPsychologyComputer scienceHuman–computer interactionPhysicsCommunicationComputer graphics (images)

Abstract

fetched live from OpenAlex

Practicing is very important in the process of learning physics. Experiencing physics laws and observing the phenomenon in the experiments and labs help students learn. However, some contexts like the law of orbits in physics cannot be practiced directly and students can only learn it from animation or drawings. We have designed a Kinect game for students to experience orbital physics and conducted a pilot in a summer camp of Athabasca University's science outreach program to verify the hypotheses include whether the students' attitudes toward computer/video games will affect their perceptions toward the developed Kinect game or not, and whether their performance in the game will be influenced by the lack of prior knowledge of the law of orbits or not. The quantitative analysis results showed that there was a positive correlation between students' gaming performances and what they knew about the relevant physics knowledge. Also, it shows that the students' attitudes toward computer/video games do not affect their perceptions toward the developed Kinect game in terms of its usability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.062
GPT teacher head0.342
Teacher spread0.280 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations6
Published2015
Admission routes1
Has abstractyes

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