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Record W2998477998 · doi:10.1145/1151954.1067477

Iconic programming for flowcharts, java, turing, etc

2005· article· en· W2998477998 on OpenAlexaff
Stephen Chen, Stephen Morris

Bibliographic record

VenueACM SIGCSE Bulletin · 2005
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceProgramming languageJavaSymbol (formal)FlowchartProgrammerSyntaxExecutableTuringArtificial intelligence

Abstract

fetched live from OpenAlex

One of the largest barriers to learning programming is the precise and complex syntax required to write programs. This barrier is a key impediment to the integration of programming into the core curriculum of general high school science courses - there is not enough time to learn both syntax and programming in a three-week course module. The newly developed "Iconic Programmer" allows executable programs to be written through mouse clicks and menus, includes symbol by symbol translation into Java and Turing, and comes complete with a three-week lesson plan suitable to new programmers. To date, the new tool has been used effectively with full-semester, introductory programming courses at both the university and high school level.

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)0.001
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.134
Threshold uncertainty score0.449

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1340.054

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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
GenreMethods

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

Citations7
Published2005
Admission routes1
Has abstractyes

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