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Record W4415030766 · doi:10.59944/postaxial.v3i4.494

The Professionalism of Islamic Religious Education Teachers in Facing the Challenges of Digitalized Learning

2025· article· en· W4415030766 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

VenueInternational Journal of Post Axial Futuristic Teaching and Learning · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Character Development
Canadian institutionsCentre for Social Innovation
Fundersnot available
KeywordsIslamDocumentationDigital literacyReligious educationQualitative researchFace (sociological concept)LiteracyIslamic education

Abstract

fetched live from OpenAlex

The transformation of digital education presents both challenges and opportunities for Islamic Religious Education (PAI) teachers, particularly in shaping students’ character. This study aims to examine the impact of learning digitalization on the role of PAI teachers, the challenges they face in mastering technology, and the strategies for developing digital-based learning at Tahfizhul Quran Al Kautsar School in Klaten. This research employs a qualitative case study approach through interviews, observations, and documentation conducted in June 2025. The findings indicate that digitalization has shifted the role of teachers to become more active facilitators. However, challenges such as low digital literacy and limited infrastructure remain significant obstacles. Solutions include training, the development of Islamic digital teaching materials, the use of appropriate platforms, and communication with students’ guardians. Collaboration among teachers, schools, and relevant stakeholders is essential to create innovative yet value-driven Islamic education.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
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 integrity0.0000.001
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 head0.013
GPT teacher head0.344
Teacher spread0.331 · 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