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Record W3021509599 · doi:10.1080/03323315.2020.1779108

Framework for analysing continuity in students’ learning experiences during primary to secondary transition in mathematics

2020· article· en· W3021509599 on OpenAlexfundno aff
Ian Cantley, Niamh O’Meara, Mark Prendergast, Lorraine Harbison, Clare O’Hara

Bibliographic record

VenueIrish Educational Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsnot available
FundersQueen's UniversityIrish Research CouncilQueen's University Belfast
KeywordsTransition (genetics)Mathematics educationPrimary (astronomy)MathematicsPedagogySociologyPhysicsChemistry

Abstract

fetched live from OpenAlex

The transition from primary to secondary education tends to have deleterious effects on student achievement and motivation in mathematics, and these effects have been significantly linked to lack of curricular and pedagogical continuity at transition. Curricular and pedagogical practices in each phase of schooling are influenced by a number of factors including, for example, teachers’ mathematical knowledge for teaching, and a range of other school and societal level characteristics. We propose a novel theoretical framework for studying continuity of learning experiences during primary/secondary transition in mathematics which takes cognisance of these factors. The framework is based on aspects of the so-called anthropological theory of didactics, which acknowledge that mathematics learning and teaching are human activities that cannot be divorced from the broader organisational, societal and cultural contexts within which they occur; teacher attributes; and Dewey’s principle of continuity of experience. Potential applications of the framework to other forms of educational transition are also signposted.

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)0.007
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.004
Science and technology studies0.0030.010
Scholarly communication0.0050.005
Open science0.0030.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.091
GPT teacher head0.446
Teacher spread0.355 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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