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Record W3161681695 · doi:10.61805/fahma.v18i2.65

PEMANFAATAN FIREBASE REALTIME DATABASE PADA APLIKASI PEMBELAJARAN AGAMA ISLAM MENGGUNAKAN FRAMEWORK FLUTTER

2023· article· en· W3161681695 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Informatika Komputer Bisnis dan Manajemen · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Methods and Impacts
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsLaptopComputer scienceProcess (computing)MultimediaMobile deviceIslamDatabaseOperating systemWorld Wide Web

Abstract

fetched live from OpenAlex

Smartphone is now a device that is in great demand by children. In addition to playing, smartphones can be used for learning for children. Making mobile applications is often considered difficult because it requires high laptop specifications, but with all the flutter resolved. 
 Firebase is able to connect mobile applications to cloud storage. One feature of firebase is the realtime database. This is very suitable to be applied for learning applications that require changing the theory periodically. 
 The results showed that the application of Islamic learning for children using flutter framework can help the process of learning Islam because it can be done anywhere. Theory and questions can change automatically without having to be refreshed by the user.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.050
GPT teacher head0.375
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