MétaCan
Menu
Back to cohort
Record W7117713467 · doi:10.46328/ijemst.5315

<b>Geometric Transformations and Symmetry in Primary and Secondary Education: A Review of Themes, Media, and Theoretical Frames from 1990 to Present </b>

2025· article· W7117713467 on OpenAlexaff
J. Enrique Hernández-Zavaleta, Sandra Becker, Douglas B. Clark, Corey Brady

Bibliographic record

VenueInternational Journal of Education in Mathematics Science and Technology · 2025
Typearticle
Language
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsUniversity of CalgaryCape Breton University
Fundersnot available
KeywordsSymmetry (geometry)Focus (optics)Field (mathematics)Key (lock)Set (abstract data type)Concept learningTransformation geometry

Abstract

fetched live from OpenAlex

There is need for increased focus on geometric transformations and symmetry in primary and secondary education, as well as increased research to support learning and teaching of geometric transformations. To identify directions for future research and teaching, we set out to map the research that has already been conducted and to identify key areas of focus and opportunity going forward. Toward these goals, this systematic review examines 62 peer-reviewed articles on teaching and learning about 2D geometric transformations and symmetry since 1990. To guide our review, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The review explores the research in terms of: (a) how students learn about transformations, (b) how teaching about transformations has been conceptualized, (c) how media have been leveraged to support learning about transformations, and (d) which theoretical frames have been leveraged and how have those frames shifted over time. Discussion and conclusions consider key areas of growth for the field going forward to better support teachers and students learning about symmetry and transformations.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.341
Teacher spread0.331 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Education in Mathematics Science and TechnologySame topicMathematics Education and Teaching TechniquesFrench-language works237,207