Correlates of Success in Introductory Programming: A Study with Middle School Students
Bibliographic record
Abstract
<p>The demand for computing professionals in the workplace has led to increased attention to computer science education, and introductory computer science courses have been introduced at different levels of education. This study investigated the relationship between gender, academic performance in non-programming subjects, and programming learning performance among middle school students with no prior programming experience who took an introductory programming course. We found that girls performed as well as or even better than boys in introductory programming among high-ability Chinese middle school students. However, we found that, instead of gender, students’ performance differences in programming were better explained by their academic performance in non-programming subjects. Students’ math ability was strongly related to their programming performance, and their English ability was the best predictor of their success in introductory programming for these Chinese students. Findings confirm previous studies that have shown a relationship between students’ math ability and performance in learning to program, but the relationship between English ability and introductory programming was unexpected. While this relationship may be specific to students whose first language is not English, aspects of native language may pose hidden barriers that might affect all students’ success in introductory programming.</p>
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".