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Record W4220858181 · doi:10.5430/wjel.v12n2p239

E99: Desktop Game Application for Learning Asmaul Husna

2022· article· en· W4220858181 on OpenAlex
Jasni Ahmad, Della Maudy Mahardika

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsMemorizationComputer scienceFormative assessmentHuman–computer interactionBoredomUsabilityGame based learningProcess (computing)MultimediaMathematics educationPsychology

Abstract

fetched live from OpenAlex

Nowadays, students still use conventional methods to learn about the Asmaul Husna, the use of technology has not been fully utilized. This causes difficulty in understanding of students in the learning process. Moreover, causing boredom in memorizing Asmaul Husna. Need appropriate learning media needed so students become more interested in memorizing the Asmaul Husna. Therefore, the purpose of this study is to design and develop the game to introduce Asmaul Husna in the form of the game called “E99” a game application of learning Asmaul Husna. This study involves 20 students aged 7 – 14 years old. The method used to carry out formative evaluation is the quantitative method and Perceived Usefulness and Ease of Use (PUEU) and Questionnaire for User Interface Satisfaction (QUIS) as an instrument. Overall findings show this application 89% useful, 83.75% ease of use and 85.5% satisfying. It indicates E99 is effective game for help students recognize and memorize Asmaul Husna.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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