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Record W4394621875 · doi:10.14324/lre.22.1.11

Developing teachers’ cultural competencies through co-design of robot-coding mathematics activities for Latinx and Black elementary school students

2024· article· en· W4394621875 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLondon Review of Education · 2024
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
FundersNational Science Foundation
KeywordsCultural competenceMathematics educationCoding (social sciences)PedagogyPsychologySociologySocial science

Abstract

fetched live from OpenAlex

This year-long case study involved the professional development of teachers in New York City elementary schools, who co-designed with researchers culturally relevant robot-coding mathematics activities to advance teachers’ understanding of culturally responsive mathematics pedagogy. Study findings indicated that co-designing culturally relevant robot-coding mathematics activities led to the development of teachers’ cultural competencies, deeper understanding of culturally responsive mathematics pedagogy and their students’ cultures, stronger agency, and ability to integrate culturally responsive pedagogy into their mathematics curriculum. Teachers also began to perceive the robot as a mathematical tool rather than a motivational add-on, and started to develop their own cultural lens while focusing less on school structure constraints. The study emphasises the importance of engaging teachers as active co-designers of culturally relevant coding curriculum in professional development programmes.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.772
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.064
GPT teacher head0.390
Teacher spread0.327 · 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