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Record W4407765961 · doi:10.3390/educsci15030265

Examining the Potential of a University-Accredited Islamic Education Teacher Training Program: A Conceptual Exploration

2025· article· en· W4407765961 on OpenAlexaffabout
Asma Ahmed

Bibliographic record

VenueEducation Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsNiagara College
Fundersnot available
KeywordsAccreditationIslamTraining (meteorology)PsychologyMathematics educationTeacher educationMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

Public schools (K-12) are experiencing a remarkable decline in enrollment across Canada. More and more parents are choosing independent schools for their children’s education. Muslim parents, in particular, are transferring their children to Islamic schools as they are increasingly losing faith in public schools. Muslim students in the public school systems, wherever they are on the continuum of practice—from secular to orthodox—do not perceive their schools to be responsive to their religious beliefs, values, behaviours, and practices. However, Islamic schools are stuck in normative, secular, and reductive pedagogies, with most, if not all, Islamic teachers lacking training in Islamic pedagogy. This article is a conceptual exploration of various approaches to offering an Islamic teacher training program in Canada by an accredited university, including reintroducing the Islamic Teacher Education Programme (ITEP), which offered a one-year professional learning certificate. Another approach is establishing a stream in teacher education programs similar to the Catholic stream. The article serves as a stepping stone to initiate dialogue and collaborative efforts toward creating a comprehensive approach that includes all stakeholders tailored to the unique needs of Islamic school teachers in Ontario, Canada.

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 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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.106
GPT teacher head0.379
Teacher spread0.273 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2025
Admission routes2
Has abstractyes

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