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Record W2532627171 · doi:10.1109/vs-games.2016.7590337

Analysis of Game and Learning Mechanics According to the Learning Theories

2016· article· en· W2532627171 on OpenAlexaff
Azeneth Patiño, Margarida Roméro, Jean-Nicolas Proulx

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGame mechanicsComputer sciencePerspective (graphical)InteractivityProcess (computing)Game based learningArtificial intelligenceMultimedia

Abstract

fetched live from OpenAlex

In digital game based learning (DGBL), game mechanics and learning mechanics define the interactivity possibilities between the player and the game. While game and learning mechanics can facilitate the design and development of DGBL activities, they have not yet been analyzed from the perspective of the learning theories underlying the type of interactions between the player and the game. This study aims to identify learning theories that sustain game and learning mechanics. This micro-level approach is therefore necessary in order to identify the process, results and impacts of game mechanics in DGBL.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.799

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)0.0010.000

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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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.

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

Citations17
Published2016
Admission routes1
Has abstractyes

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