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Record W2889423741 · doi:10.5206/cie-eci.v47i1.9323

Reciprocal learning in mathematics education: An interactive study between two Canadian and Chinese elementary schools

2018· article· en· W2889423741 on OpenAlexafffundvenueabout
Aihui Peng, Anthony N. Ezeife, Bo Yu

Bibliographic record

VenueComparative and International Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Windsor
FundersNational Social Science Fund of ChinaSocial Sciences and Humanities Research Council of Canada
KeywordsDichotomyMathematics educationReciprocalReciprocal teachingPedagogySociologyPsychologyMathematicsPolitical scienceReading comprehension

Abstract

fetched live from OpenAlex

In this study, the researchers go beyond the back-and-forth debates on the East-West educational paradigms that often arise from comparative studies, and take a reciprocal learning approach to explore in-depth the commonalities and differences in mathematics education between two Canadian and Chinese elementary schools. Research data were collected through direct and indirect interactions between the pair of research schools, including Skype meetings; formal and informal conversations with teachers and administrators; and the sharing/exchange of documents, texts, teaching materials, and resources. Results show that there is a common emphasis on some thematic issues in the teaching and learning of mathematics including the use of manipulatives, multiple solutions to mathematical problems, and parental involvement, but also some differences between the two schools in teachers’ strategies for teaching problem solving, students’ learning tendencies and schools’ supports for Special Needs students. The researchers conclude that the dichotomies of the East-West educational paradigms need to be further, and more deeply re-examined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.220
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.209
GPT teacher head0.519
Teacher spread0.310 · 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 teacher head, 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

Citations8
Published2018
Admission routes4
Has abstractyes

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