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Integrating Computational Thinking and Mathematics

2019· book-chapter· en· W2999835004 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2019
Typebook-chapter
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsWestern University
Fundersnot available
KeywordsComputational thinkingCreativityMathematics educationCoding (social sciences)The artsCritical thinking21st century skillsAxial codingPedagogyComputer sciencePsychologyQualitative researchGrounded theorySociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Globally, computational thinking and coding in schools has become more popular as well as a growing area of interest in education reform. Coupling coding with creative thinking promises to meaningfully engage students in their learning and to improve their coding and computational thinking skills. This prompts discussions about STEAM (Science, Technology, Engineering, Arts, and Mathematics), which promotes creativity and innovation through the integration of the arts in STEM subjects. This study addresses the following question: What mathematics and computational thinking do students learn through different models of STEAM education in non-profit and in-school contexts? A small sample was taken of four different STEAM programs in Ontario, Canada. We carried out a qualitative case study with 103 participants, 19 adults and 84 students. The findings from this study have implications for designing, implementing and researching K-8 STEAM programs that promote coding and computational thinking skills in the context of learning mathematics.

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 score0.261
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 studies0.0000.000
Scholarly communication0.0000.002
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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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