Enrollment in a Library Credit Course is Positively Related to the College Graduation Rates of Full Time Students
Notice bibliographique
Résumé
A Review of: Cook, J.M. (2014). A Library credit course and student success rates: A longitudinal study. College & Research Libraries 75(3), 272-283. doi:10.5860/crl12-424 Abstract Objective – To determine the impact of a credit-bearing information literacy skills course on student success rates. Design – Observational Study. Setting – An academic library at a mid-sized university in Georgia, United States of America. Subjects – Nine cohorts of students (n=15,012) who entered the institution for the first time, on a full-time basis, each year between 1999 and 2007. Methods – Aggregate data on each student cohort was gathered from the Department of Institutional Research and Planning. Data included high school ACT and SAT scores, high school graduating GPAs, college graduating GPAs, and college graduation dates. The nine cohorts were each divided into two groups: students who took a credit library course (LIBR 1101) at some point during their student career, and students who did not. For each cohort, a Pearson Chi-Square test was used to determine statistical correlation between library course enrollment and four-, five-, and six-year graduation rates. Z-tests were used to determine a difference in the average graduation GPA of students who did and did not take the course, as well as a difference in the average high school graduation GPA, ACT, and SAT scores of the two groups in each cohort. Main Results – Graduation rates were positively associated with students who took the library course at some point during their studies. Students who took the library course graduated at higher rates than students who did not: 56% of those students who took the library course graduated within the study’s time frame, compared to 30% of those who did not take the course. On average, there was no significant difference in college graduation GPAs between students who did and did not take LIBR 1101. During the time period of the study, more students who took the course graduated than those who did not, but those students who took the course did not have higher graduating GPAs. Conclusion – Students who enrolled in LIBR 1101 at some point in their studies graduated at a significantly higher rate than students who did not.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».