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Record W2622932377 · doi:10.5430/wje.v7n3p21

The Impact of Educational Games-Based iPad Applications on the Development of Social Studies Achievement and Learning Retention among Sixth Grade Students in Jeddah

2017· article· en· W2622932377 on OpenAlexvenueno aff
Hanan A. Najmeldeen

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyTest (biology)Academic achievementTeaching methodAchievement testStandardized test

Abstract

fetched live from OpenAlex

The present study aims to evaluate the impact of educational games-based iPad applications on the development ofsocial studies achievement and learning retention. Sample consisted of (48) sixth grade primary students in Jeddah.The author adopted Quasi-experimental design of the experimental and control groups. She also provided the teachera guidebook that explains teaching method using educational games-based iPad applications. Pre-post achievementtest was applied to both groups. Delayed achievement test, which evaluates learning retention, was applied fourweeks after post-test. The study showed the impact of educational games-based iPad applications on achievementand learning retention with statistically significant differences in scores means of both groups' participants in posttestand delayed test in favor of the experimental group. She recommended using iPad educational applications,which she prepared, in teaching.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.068
GPT teacher head0.435
Teacher spread0.368 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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