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Record W2626624493

Attitudes Regarding Mathematics Teaching and Learning in Canada and China

2017· article· en· W2626624493 on OpenAlexaffabout
Sijia Zhu, Doug McDougall

Bibliographic record

Venue2017 Conference of the Canadian Society for the Study of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsMathematics educationChinaSubject (documents)Teaching methodReciprocalPedagogyPsychologyComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Ones attitudes regarding their mathematics abilities have a significant effect on their achievement in teaching and learning the subject. This paper describes the research undertaken with five Canadian and Chinese teacher pairs and their students, focusing specifically on the differences ands similarities between their attitudes and beliefs in mathematics teaching and learning. Data was gathered through formal and informal interactions during school visits, online conference calls, emails, instant messages and pen pal communications between teachers and students. Through the development of reciprocal learning relationships, the teachers in both Canada and China have been able to deeply reflect upon their own attitudes and beliefs in mathematics thus allowing them to identify how their own personal anxieties and attitudes maybe affecting their teaching. During the video lessons studies of their reciprocal learning partners teaching, teachers from both nations reflected about their own personal experience with mathematics teaching and learning in comparison to their teaching partners.  In particular, Canadian teachers focus on the mathematics content knowledge of the Chinese teachers while the Chinese teachers focus on pedagogy of the Canadian teachers. In both countries, students were immensely curious about the others culture, school environment and learning habits.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
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 head0.127
GPT teacher head0.370
Teacher spread0.243 · 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.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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