Relationships Between Student Characteristics, Academic Advising and College Student Success
Notice bibliographique
Résumé
This dissertation is about the student success problem: institutional retention rates have remained low for decades and clear evidence about why and how to support more students to persist and graduate is elusive. In Canada, and within Ontario Colleges specifically, there is a dearth of research on this topic. Performance-based funding and the global pandemic has increased the need to improve these outcomes. The three studies that comprise this dissertation investigate the relationships between the characteristics of students at the time they enter college, participation in academic advising, and student success. In the first study, an integrated literature review method was used to analyze two decades of peer-reviewed research related to five constructs – program fit, career clarity, academic self-efficacy, educational commitment, and friend and family support – and the relationships with both advising and student success. Results showed positive relationships. New working definitions for each construct were developed. The second and third studies used a unique administrative dataset from Mohawk College. In the second study, a series of factor analyses identified three latent variables – Career and Program Clarity, Friend and Family Support, and Positive Academic Attitudes – within the Mohawk College student entrance survey. The latent variable measurement model was used as the foundation of a structural equation model (SEM) as part of the third study to analyze the relationships between the latent variables, advising participation, and student success. The SEM did not produce an acceptable fit or find any significant relationships. The concluding chapter used an integrated discussion method to summarize the overall findings. The contributions to the literature include an example of unique methods; new findings related to student success; and evidence of the practical use of college administrative data. Seven implications and next steps for researchers, practitioners and leaders are identified: improving institutional data collection practices; more focused evaluation of academic advising; purposeful outreach to students who do not engage early in admissions or transition processes; continued efforts to work with students to (re)define student success; new investments in research infrastructure; stronger emphasis on good research methods; and new campus commitments to relationship rich education.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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 ».