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Record W3046992986 · doi:10.25071/1916-4467.40576

Computational and Mathematics Thinking Workshops for Elementary School Children and Their Parents

2020· article· en· W3046992986 on OpenAlexaffvenue
Rawia Zuod, Immaculate Kizito Namukasa

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2020
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsWestern University
Fundersnot available
KeywordsContext (archaeology)SituatedMathematics educationCurriculumSession (web analytics)Strict constructionismQualitative researchSituated learningPedagogyPsychologyComputer scienceSociologyEpistemologyBiology

Abstract

fetched live from OpenAlex

This qualitative study explores the nature of engagement of students in Mathematics Thinking (MT) activities in the context of Computational Thinking (CT) integration. It specifically investigates the ways that students interact during CT and MT activities. This study uses a constructionist framework of learning by making and is situated in literature on integration of CT in the mathematics curriculum. In this case study, observations, interviews and reflection data were collected from ten students during CT and MT workshops. The data were analyzed to determine the ways in which CT activities enrich mathematical concepts. All children found that the CT activities (Symmetry, Sphero and Scratch) enriched their understanding of mathematical concepts. Several of the children were excited about what they referred to as a more interesting and interactive way of learning math and code. This study was limited to Grade 3 to Grade 6 students in a private school. For future research, the researchers suggest conducting a study in public schools that will involve specific tools of CT. The researchers also recommend conducting CT workshops over a three-day period so that children do one activity each day rather than all three activities in one session.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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 designObservational
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
Published2020
Admission routes2
Has abstractyes

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