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Record W3209654791 · doi:10.5430/jct.v10n4p1

Perceptions of Teachers and Subject Advisers Regarding Curriculum Development Processes in Fort Beaufort District, Eastern Cape

2021· article· en· W3209654791 on OpenAlexvenueno aff
Uloma Nkpurunma, Ignatius Khan Ticha

Bibliographic record

VenueJournal of Curriculum and Teaching · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumBeaufort scaleSubject (documents)CapePedagogyQuality (philosophy)Focus groupPerceptionCurriculum developmentPsychologyMathematics educationMedical educationSociologyGeographyLibrary scienceMedicineArchaeologyComputer science

Abstract

fetched live from OpenAlex

This study was designed to investigate the perceptions of teachers and subject advisers regarding the curriculum development processes in Fort Beaufort District in the Eastern Cape, South Africa. The sample consisted of twenty-two respondents: four principals, twelve teachers and six subject advisers. Data were collected through structured face-to-face interviews, focus group discussions and document analysis. The findings revealed that both teachers and subject advisers were concerned about the quality of teachers and learners as well as delivery of the curriculum. They also expressed concern about the quality of workshops; pointing to how much participation from teachers and subject advisers occurs in these workshops. Their responses create space for the researcher to engage with the question, does their involvement in these workshops help them to understand the curriculum and implement it better? Hence, this study recommends that the quality of teachers, learners and subject advisers should be considered while carrying out curriculum development processes.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.343
Teacher spread0.306 · 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 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
Published2021
Admission routes1
Has abstractyes

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