Exploring persistence in science in CEGEP : toward a motivational model
Notice bibliographique
Résumé
There is currently a shortage of science teachers in North America and continually decreasing rates of enrollment in science programs. Science continues to be the academic domain that sees the highest attrition rates, particularly for women. The purpose of the present study was to examine male and female students' experiences in mathematics and science courses during a crucial time in their academic development in an attempt to explain the high attrition rates in science between the last year of high school and the first year of CEGEP (junior college). In line with self-determination theory (Deci & Ryan, 1985), as well as achievement-goal theory (Pintrich & Schunk, 1996) and research on academic emotions, the study examined the relation between a set of motivational variables (i.e., perceptions of autonomy-support, self-efficacy, achievement goals, and intrinsic motivation), affect, achievement, and persistence. A secondary objective was to test a motivational model of student persistence in science using structural equation modeling (SEM). The sample consisted of 603 male and 706 female students from four English-language CEGEPs in the greater Montreal area. Just prior to beginning CEGEP, participants completed a questionnaire that asked about the learning environment in high school mathematics and science classes as well as student characteristics including sources of motivation, personal achievement goals, and feelings of competence. All students expressed an initial interest in pursuing a career in science by enrolling in optional advanced mathematics and science courses during high school. Multivariate analysis of variance was used to examine differences among male and female students across the variables measured. Structural equation modeling was used to test the validity of a questionnaire designed specifically to gather information about CEGEP students' experiences with mathematics and science, and to evaluate the fit of a model designed to reflect the interactions between the different variables. Students' experiences during high school have an impact on their decisions to pursue or abandon their path toward an eventual science career. Classroom experiences and student characteristics interact to influence their performance and affect, which in turn influence their decisions. Implications for promoting persistence in science are discussed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».