Lessons learned and recommended strategies for game development components in a computer literacy course
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.
Bibliographic record
Abstract
The challenges that instructors face attempting to motivate novice programming students are amplified when the students are not pursuing degrees or careers in computer science. For the programming module of our course for non-computer science majors we assigned a video game programming deliverable that we expected would engage students and enhance their experiences. After extensive analyses of the survey responses of 245 enrolled students we were surprised to learn that, although the majority believed the game programming experience enhanced their learning overall, another majority reported that the project itself was not enjoyable. Through qualitative analysis we have identified several key areas that seem to have detracted from the overall level of enjoyment, and in this paper we follow this investigation with discussion surrounding how these issues could be remedied in the future. These recommended strategies will bolster student enjoyment and motivation in future offerings and we believe this discussion will prove very useful to other instructors planning to employ game programming components.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it