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Record W2803006329 · doi:10.4204/eptcs.270.2

Using Elm to Introduce Algebraic Thinking to K-8 Students

2018· article· en· W2803006329 on OpenAlexafffund

Bibliographic record

VenueElectronic Proceedings in Theoretical Computer Science · 2018
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCurriculumComputer graphicsSyntaxGraphicsDomain (mathematical analysis)Focus (optics)Recursion (computer science)Inclusion (mineral)

Abstract

fetched live from OpenAlex

In recent years, there has been increasing interest in developing a Computer Science curriculum for K-8 students. However, there have been significant barriers to creating and deploying a Computer Science curriculum in many areas, including teacher time and the prioritization of other 21st-century skills. At McMaster University, we have developed both general computer literacy activities and specific programming activities. Integration of these activities is made easy as they each support existing curricular goals. In this paper, we focus on programming in the functional language Elm and the graphics library GraphicSVG. Elm is in the ML (Meta Language) family, with a lean syntax and easy inclusion of Domain Specific Languages. This allows children to start experimenting with GraphicSVG as a language for describing shape, and pick up the core Elm language as they grow in sophistication. Teachers see children making connections between computer graphics and mathematics within the first hour. Graphics are defined declaratively, and support aggregation and transformation, i.e., Algebra. Variables are not needed initially, but are introduced as a time-saving feature, which is immediately accepted. Since variables are declarative, they match students' expectations. Advanced students are also exposed to State by making programs that react to user taps or clicks. The syntax required to do so closely follows the theoretical concepts, making it easy for them to grasp. For each of these concepts, we explain how they fit into the presentations we make to students, like the 5200 children taught in 2016. Finally, we describe ongoing work on a touch-based Elm editor for iPad, which features (1) type highlighting (as opposed to syntax highlighting), (2) preservation of correct syntax and typing across transformations, (3) context information (e.g. displaying parameter names for GraphicSVG functions), and (4) immediate feedback (e.g. restarting animations after every program change).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0200.008

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 head0.309
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2018
Admission routes2
Has abstractyes

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