A Study of Non-computing Majors' Growth Mindset, Self-Efficacy and Perceived CS Relevance in CS1
Bibliographic record
Abstract
As the demand for programming skills in today’s job market is rapidly increasing for disciplines outside of computing, CS courses have experienced spikes in enrollment for non-majors. Students in disciplines including art, design and biological sciences are now often required to take introductory CS courses. Previous research has shown the role of growth mindset, self-efficacy and relevance in student success within CS but such metrics are largely unknown for non-majors. In this thesis, we surveyed non-majors in CS1 at Cal Poly, San Luis Obispo during the early and late weeks of the quarter to gain insights on their growth mindset, their self-efficacy and the perceived relevance of the course to their lives. In our analysis, we discovered that non-majors’ levels of growth mindset and of self-efficacy decreased throughout the duration of CS1 with additional differences by gender. However, non-majors largely found that the material covered in CS1 was highly relevant to their academic and professional careers despite being challenged by it. These findings provide important insights into the experiences of non-majors learning to code and can help better serve a more diverse population of students.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".