Designing an Introductory Programming Course to Improve Non-Majors' Experiences
Bibliographic record
Abstract
Demand for computing courses from students in disciplines outside of Computer Science is growing. This growth has created increasing challenges in offering one-size-fits-all CS1 courses. We found that non-CS majors' experiences and outcomes in our existing CS1 course were worse than those of intended CS majors. In response, we developed an introductory programming course, CS0.5, aimed at meeting the needs of the diverse population of non-CS major students interested in our courses. In this paper, we present the motivation, curriculum design, and evidence of effectiveness for this new course. We describe the specific design decisions we made in response to the experiences of non-CS majors in CS1. We also demonstrate that students' outcomes in CS0.5--measured in terms of students' pass rates, satisfaction, and attitudes--all not only improve compared to non-CS majors in CS1, but also largely match those of CS majors in CS1. Finally, we present student feedback, gathered through surveys and Appreciative Inquiry focus groups, that illustrates how our curriculum design choices better meet our non-major students' needs. The most-valued course design elements, as identified by focus group participants, provide insight for other CS educators who are designing similar courses.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".