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Record W2917386451 · doi:10.1145/3287324.3287496

Program Wars

2019· article· en· W2917386451 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsComputer scienceGame programmingVocabularySyntaxComprehensionVideo game designHuman–computer interactionProgramming languageGame DeveloperMultimediaArtificial intelligenceGame designGame design document

Abstract

fetched live from OpenAlex

Although there are many computer science learning games with the goal of teaching programming, such games typically require the person to either learn an existing programming language or the game's own specialized language. This can be intimidating, confusing or frustrating for an individual when they cannot get their "program" to work correctly (e.g. syntax error, infinite loop). Additionally, such games commonly use a puzzle-solving approach that does not appeal to some demographics. This paper presents a programming-language-independent approach to teaching fundamental programming and cybersecurity concepts using simple vocabulary. This approach also uses the familiar activity of playing cards against opponents to create a more dynamic and engaging learning experience. The approach is demonstrated by a web-based game called Program Wars. Results from a user study show that players are able to effectively connect game concepts to actual programming language structures; however, whether players' comprehension of computer programming is improved is unclear.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.979
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

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