Predictors of Student Success In Supplemental Instruction Courses at A Medium Sized Women’s University
Notice bibliographique
Résumé
Supplemental Instruction (SI) is a program that seeks to improve studentsuccess by targeting classes with high failure rates, as defined with a failurepercentage of 30% or more. It isorganized by an administrative SI supervisor who supervises SI leaders, whichare students that have successfully completed the courses that they have beenassigned. The SI supervisor alsocollaborates with the course instructors who aid in screening the competency ofthe SI leaders. Improvedself-confidence, teamwork, independence and course performance have beenreported as benefits of SI. This projectsought to explore the effect of SI on success and failure, along with gender,age and race. The type of course wasalso used as a factor in order to control for it as a confounding variable. In order to ascertain the effect of thesevariables on success, a technique called logistic regression was used. Caucasian female students who tookbacteriology and did not attend SI were used as the reference group. Students were about twice as likely tosucceed if they completed the required number of SI sessions and one fifth aslikely to succeed if they were in a SI class and did not meet the minimumnumber of sessions. Hispanic studentswere 40% as likely to succeed, and African American students were about onethird as likely to succeed when compared to Caucasian students. Students between20 and 29 years old were half as likely to succeed, and those 30 or older wereone quarter as likely to succeed when compared to teen students. Those in algebra were about three times morelikely to succeed than those in bacteriology, chemistry and statistics. When the students that withdrew were removed,the chances of success were about the same, except for African Americanstudents which were one quarter as likely to succeed, and those that did notmeet minimum sessions were one quarter as likely to succeed. The model explained more variation when thestudents that withdrew were included. AsSI had a strong influence on success, it should be considered as a tool toenable retention of students in high risk courses.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».