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Record W2923234925

Arguing for access: Teachers’ perspectives on the use of argumentation in elementary mathematics and its impact on student success

2019· article· en· W2923234925 on OpenAlexaff
Cathy Marks Krpan, Gurpreet Sahmbi

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsArgumentation theoryMathematics educationMathematical practiceElementary mathematicsPedagogyPsychologyEpistemology
DOInot available

Abstract

fetched live from OpenAlex

In order for students to become insightful mathematical thinkers, they need opportunities to think deeply about the concepts they are learning and engage in rich mathematical discussions.  In an effort to support teaching practices that facilitate student thinking and discourse in mathematics, this study explored teacher perspectives on the use of argumentation in elementary mathematics classrooms, and is part of a broader study on mathematical argumentation. Data for this paper was collected from six elementary teachers, one librarian, and approximately 110 students, and included research meetings, journals, and interviews. Findings suggested that while these teachers had limited experience with mathematical argumentation prior to participating in the study, and were concerned about whether their students would be able to develop mathematical arguments, by the end of the study, they felt positively towards this type of pedagogy. Further, they perceived a positive impact on their students in that more students were able to access and participate in mathematics, and achieve success. They also felt that students’ understanding of mathematics was deepened due to the use of argumentation tasks. This study suggests that mathematical argumentation is both feasible and applicable to elementary grade levels, and can have an impact on student achievement

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.124
GPT teacher head0.446
Teacher spread0.322 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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