Fostering High School Girls' Interest and Attainment in Computer Science
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
Computing education and careers are male dominated. Identifying strategies to reduce this gender gap would create a more diverse and inclusive workforce, and would respond to the growing importance of computing and technology in our society. This paper presents an intervention designed and conducted in a post-secondary polytechnic institution aimed at inspiring and motivating computer science education among high school girls. Grounded in theory and related research, this intervention was designed to address aspects recognized as having relevance to girls. Using the expectancy-value theory of motivation developed by Eccles as a theoretical foundation, this study explores the intervention's impact on participants' interest and attainment in computer science. Twenty five students (nineteen girls) from local high schools participated in this pre-questionnaire, intervention, post-questionnaire quasi-experimental study. Participants were mentored by post-secondary students (at least one mentor for each pair of participants) through activities including writing an algorithm, coding, exploring an AR/VR technology and practicing programming skills with an educational game. Analysis of resulting data revealed that girls who participated in this study experienced a high level of enjoyment, increased interest, perceived positive learning gains, and were inspired by their post-secondary mentors. Post-questionnaire responses indicated that girls improved their ability beliefs and reduced their stereotypical views.
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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 | 0.002 |
| 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