Constraints Influencing the Efficacy of a PGCE Mathematics Program: A Case Study
Bibliographic record
Abstract
The learning attainment of South African learners in mathematics is, as The International Mathematics and Science Study (TIMSS) data reveal, far from optimal. A key contributing factor is a shortage of competent and confident qualified mathematics teachers. The Post Graduate Certificate in Education (PGCE) offered at South African universities is a key qualification designed to address this challenge (amongst others, such as the Bachelor of Education [B.Ed.]). However, the success of the PGCE as a preparation for mathematics teachers is not without concern, as this paper argues. Using a qualitative case study focusing on a PGCE with a mathematics focus offered by a university of technology, this paper discusses the constraints identified by prospective teachers and teacher educators. Constraints are explored by focusing on the curriculum, partnerships, and policy relating to the delivery of the PGCE as true and critical to the efficacy of the program. The paper contributes to context-specific understanding of the constraints influencing the PGCE’s efficacy (as offered in 2014) in developing newly qualified teachers’ (NQTs) skills and knowledge to be confident and competent mathematics teachers. Keywords: Post Graduate Certificate in Education; mathematics; learning to teach; governance; curriculum. Les données d’une enquête internationale portant sur les acquis scolaires en mathématiques et en sciences (TIMSS) révèlent que le rendement des apprenants sud-africains en mathématiques est loin d’être optimal. Un facteur qui contribue de façon significative à ce phénomène est le manque d’enseignants qualifiés qui sont compétents et confiants. Le certificat d’études supérieures en éducation (PGCE) offert dans les universités en Afrique du Sud est une qualification importante conçue pour faire face à ce défi (le Baccalauréat en Éducation, entre autres). Toutefois, la réussite du PGCE comme outil de préparation pour les enseignants n’est pas sans inquiétude, comme le soutient cet article. En s’appuyant sur une étude de cas qualitative portant sur un PGCE avec une majeur en mathématiques et offert par une université de technologie, cet article discute des contraintes identifiées par de futurs enseignants et formateurs d’enseignants. Les contraintes sont examinées en se penchant sur le curriculum, les partenariats et les politiques relatives à la prestation du PGCE pour évaluer dans quelle mesure ils sont véritables et essentiels au programme. Cet article contribue à une compréhension, propre au contexte, des contraintes qui influencent l’efficacité du PGCE (tel qu’offert en 2014) à développer les compétences et les connaissances de nouveaux diplômés pour qu’ils soient des enseignants confiants et compétents. Mots clés diplôme d’études supérieures en éducation mathématiques; apprendre à enseigner; gouvernance; curriculum
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".