Untraceable: A Videogame Designed to Teach Programming Terms and Concepts
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".