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Record W4383722445 · doi:10.21125/edulearn.2023.0278

TEACHER TRAINING ON MOBILE AND GAME-BASED LEARNING: LITERATURE REVIEW AND TRAINING PROGRAM PROPOSAL

2023· article· en· W4383722445 on OpenAlexfundno aff
Margarida Marques, Lúcia Pombo

Bibliographic record

VenueEDULEARN proceedings · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaUniversity of Illinois at Urbana-ChampaignHumanitas UniversityLiverpool John Moores UniversityUniversidade de AveiroUniversity College CorkHaute école Spécialisée de Suisse OccidentaleAix-Marseille UniversitéUniversity of HertfordshireGrand Valley State UniversityUniverza v LjubljaniUniwersytet Marii Curie-SkłodowskiejKhalifa University of Science, Technology and ResearchUniversity of BristolHelsingin YliopistoFlorida Gulf Coast UniversityUniversity of AlbertaUniversity of CyprusGeorgia Southern UniversityUniversity of HuddersfieldUniversità degli Studi di MilanoUniversity of WolverhamptonSwansea UniversityCyprus University of TechnologyUniversity of Waikato
KeywordsTraining (meteorology)Computer scienceGame based learningMultimediaMathematics educationMedical educationPsychologyMedicineGeography

Abstract

fetched live from OpenAlex

articles to IATED conferences to maintain high ethical standards.IATED shall guarantee the high technical and professional quality of the publications and that good practices and ethical standards are maintained.If unethical behaviors are identified, an investigation will be initiated, and pertinent actions will be taken.

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)0.026
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0130.008
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.002

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 head0.036
GPT teacher head0.362
Teacher spread0.326 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2023
Admission routes1
Has abstractyes

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