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Record W2971271619 · doi:10.1080/25783858.2019.1659632

Changing attitudes to mathematics in primary school teachers in England

2019· article· en· W2971271619 on OpenAlexaboutno aff
Mary McAteer, Victoria Grinyer

Bibliographic record

VenuePRACTICE · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationSchool teachersPrimary (astronomy)Primary educationPedagogyPsychologyPhysics

Abstract

fetched live from OpenAlex

Concern about attitudes to mathematics and mathematics education is not unique to England. Studies across the world, including the USA, Canada and Australia, indicate similar concerns. This research aimed to identify the factors that impacted on the formation of teachers’ attitudes to mathematics in primary schools in England and to discover how these have been shaped and/or changed during an in-service professional development programme, The Mathematics Specialist Teacher Programme (MaST). We thus take a unique position in attempting to develop a possible model for reducing teacher mathematics anxiety, and its consequences in classrooms. Findings were analysed using key typologies of professional learning and and revealreveal that teachers who undertook the programme made strong, explicit links between previous experiences of being taught mathematics, their own feelings of mathematics confidence and competence, and levels of mathematics anxiety. High-level collaboration with colleagues and expert facilitators were evident as significant factors in overcoming this anxiety.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.176
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.383
Teacher spread0.353 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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