Perceptions of non-CS majors in intro programming: The rise of the conversational programmer
Bibliographic record
Abstract
Despite the enthusiasm and initiatives for making programming accessible to students outside Computer Science (CS), unfortunately, there are still many unanswered questions about how we should be teaching programming to engineers, scientists, artists or other non-CS majors. We present an in-depth case study of first-year management engineering students enrolled in a required introductory programming course at a large North American university. Based on an inductive analysis of one-on-one interviews, surveys, and weekly observations, we provide insights into students' motivations, career goals, perceptions of programming, and reactions to the Java and Processing languages. One of our key findings is that between the traditional classification of non-programmers vs. programmers, there exists a category of conversational programmers who do not necessarily want to be professional programmers or even end-user programmers, but want to learn programming so that they can speak in the “programmer's language” and improve their perceived job marketability in the software industry.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".