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Record W2768847019 · doi:10.5430/ijhe.v6n6p44

Dimensions of Quality in Teacher Education: Perception and Practices of Teacher Educators in the Universities of Sindh, Pakistan

2017· article· en· W2768847019 on OpenAlexvenueno aff
Zafarullah Sahito, Pertti Väisänen

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityQuality (philosophy)Thematic analysisTeacher educationPedagogyNarrativeNarrative inquiryResource (disambiguation)PhenomenonQualitative researchSociologyProduct (mathematics)Mathematics educationPolitical scienceMedical educationPsychologySocial scienceMedicine

Abstract

fetched live from OpenAlex

This study was conducted to explore the dimensions of quality education in teacher education departments at universities of Sindh province of Pakistan. The qualitative research approach was employed for data collection and then analysed through thematic-narrative analysis technique. The total eight dimensions of quality were found, as two were concerned with pre-sage, four as process and two as product dimensions, known as 3Ps. The findings of this article would be found reliable resource and an addition in to the existing literature of quality education to understand the phenomenon in existing organisational setting of teacher education departments and institutions in Sindh, Pakistan. The radical reforms for educational and economic development can be brought through better understanding of the phenomenon of the quality education, which support the teacher educators, students and the heads to maintain peace and prosperity for humanity in their respective societies through quality teaching-learning process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.482
Teacher spread0.434 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations7
Published2017
Admission routes1
Has abstractyes

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