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Record W2782044847 · doi:10.25071/ryr.v3i0.40430

Untraceable: A Videogame Designed to Teach Programming Terms and Concepts

2016· article· en· W2782044847 on OpenAlexaboutno aff
Christopher Elcombe

Bibliographic record

VenueRevue YOUR Review (York Online Undergraduate Research) · 2016
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeComputer scienceMathematics educationCurriculumGraphicsMultimediaVideo game developmentPython (programming language)Game designProgramming languagePedagogyPsychologyLinguisticsComputer graphics (images)

Abstract

fetched live from OpenAlex

The “Untraceable” project is designed to teach students the concepts, themes, and terminology of computer programming within the Ontario high school curriculum. It does this through a series of logic puzzles designed to teach students in grades nine through twelve the syntax and structure of a programming language in a visual setting with an enjoyable narrative. Students are placed in a futuristic world, and guide a protagonist with psychic abilities. Players are captured by the government, and forced to develop their powers through a series of programming puzzles as they team up with another captive psychic to try to escape. The game is created using Python, Cocos2D version 0.6.0, Pyglet, and other software to generate a game with graphical and musical content. The game will be tested with computer science students in an Ontario high school to evaluate the efficacy of gaming as a teaching tool, as well as to explore the effects of graphics, sound, narrative, and gameplay as components in a learning environment. The final iteration of the project will address any flaws found by students, and a user study will offer project reflections as well as suggestions to future researchers.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.083
GPT teacher head0.395
Teacher spread0.312 · 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.

Study designOther design
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

Citations0
Published2016
Admission routes1
Has abstractyes

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