Middle and high school girls’ attitude to science, technology, engineering, and mathematics career interest across grade levels and school types
Bibliographic record
Abstract
The aim of this study is to examine Kazakh female students’ interest in STEM professions. A convenient sampling method was used to determine the participants from 10 girls’ schools in Almaty city in Kazakhstan. 522 girls from grades 7th to 11th provided answers to the “STEM Career Interest Survey” which was administered online. Collected data was analyzed to see how girls’ STEM carries interest change according to the type of school and grade level, along with locating the correlations between their interests and their end-term marks in each STEM subject. MANOVA analysis showed that girls’ career interests in different STEM subjects are changing for different school levels across types of schools. Through ANOVA analysis we showed that only girls’ math interest significantly changed across school levels. Post-hoc analyses indicated that seventh level students’ interest in math was statistically higher than eighth and ninth level students. For the school type variable, ANOVA analysis showed that only girls’ technology and engineering interests were significantly different across school types. In other words, girls in Nazarbayev Intellectual Schools (NIS) were significantly more interested in technology and engineering careers than public school girls while for science and mathematics there was no difference between the two types of schools. Additionally, at the 8th and 11th school levels NIS girls have a higher interest in science while at the 10th level public school girls have higher scores. Finally, we detected significant correlations of modest amplitude between girls’ STEM were analyzed rest and their achievement in physics, math, chemistry, and biology. This study will allow supporting teachers and school administrators in their efforts to encourage girls to pursue STEM studies and careers, and we hope it will also help researchers to orient their efforts in providing them with fertile and durable solutions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".