Middle and high school girls’ attitude to science, technology, engineering, and mathematics career interest across grade levels and school types
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Notice bibliographique
Résumé
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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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle