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Record W3096367245 · doi:10.1080/0020739x.2020.1837400

Towards an ecosystem for computer-supported geometric reasoning

2020· article· en· W3096367245 on OpenAlex
Zoltán Kovács, Tomás Recio, Philippe R. Richard, Steven Van Vaerenbergh, M. Pilar Vélez

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

VenueInternational Journal of Mathematical Education in Science and Technology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComputer scienceContext (archaeology)Set (abstract data type)Process (computing)Path (computing)Management scienceMathematics educationMathematicsEngineering

Abstract

fetched live from OpenAlex

In this study, we explore automated reasoning tools (ART) in geometry education and we argue that these tools are part of a wider, nascent ecosystem for computer-supported geometric reasoning. To provide some context, we set out to summarize the capabilities of ART in GeoGebra (GGb), and we discuss the first research proposals of its use in the classroom. While the design and development of ART have been embraced already by several teams of mathematics researchers and developers, the educational community, which is an essential actor in this ecosystem, has not provided sufficient feedback yet on this new technology. We therefore propose a concrete path for incorporating ART in the classroom. We outline a set of necessary procedures towards this goal, and we include a discussion on the benefits and concerns arising from the use of these automated tools in the mathematical learning process.

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)0.003
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 score0.298
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.404
Teacher spread0.367 · 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