MétaCan
Menu
Back to cohort

Young Children, Mathematics, and Coding

2014· book-chapter· en· W2478378977 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

VenueAdvances in educational technologies and instructional design book series · 2014
Typebook-chapter
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsWestern University
Fundersnot available
KeywordsCoding (social sciences)Mathematics educationComputer scienceCeiling (cloud)MathematicsEngineering

Abstract

fetched live from OpenAlex

In recent years, there have been renewed calls for young children to learn to code, using computer programming environments that offer low floor, high ceiling, wide walls coding experiences. That is, students engage with coding with minimal prerequisite knowledge, have opportunities to explore more complex coding concepts and problems, and can pursue many different interests and for a wide audience. This chapter considers how a low floor, high ceiling, wide walls learning environment may be used to couple coding with mathematics, so as to provide young children both a meaningful context for coding and a rich mathematics learning experience. Using cases from classroom-based research and math and coding apps currently under development, the discussion is organized around two questions: (1) How might we design low floor, high ceiling, wide walls mathematics experiences for young children? and (2) How might coding be used to model mathematics concepts and relationships?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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