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Record W4213202233 · doi:10.20474/jahss-3.1.5

Smart school: A comparative research between two Islamic countries, Malaysia and Iran

2017· article· en· W4213202233 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

VenueJournal of Advances in Humanities and Social Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIslamPolitical scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

In this paper, Smart Schools in Malaysia and Iran will be examined and compared to understand what opportunities and barriers still exist for improving the value and access of these schools. A smart school is a learning institution that uses non-traditional means of instruction, teaching, and learning where school management is focused on helping students cope with and leverage changes brought about by, the information age. The smart school program will be examined for both the state-sponsored public sector (state schools) and privately funded sector (private schools). Globalization requires a more practical education system in which outputs can work in complicated situations with modern instruments. Many developing countries prefer to establish an education system with Smart Schools to achieve education quality closer to developed countries. Successful smart schools' requirements are different from traditional schools in curriculum, pedagogy, assessment, teaching-learning material, management, visions, and stakeholder engagement. Malaysia successfully established this system from early 1996, and Iran has tried to also establish a smart school system since 2002.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
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 head0.140
GPT teacher head0.395
Teacher spread0.255 · 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