The Role of Non-Academic (Soft) Skills in Student Transition to Postsecondary Education: A Mixed Methods Study
Notice bibliographique
Résumé
This study is a sequential mixed methods explanatory examination of the role of soft (non-academic) skills in shaping student transition from high school to postsecondary education (PSE). Specifically, this study looked at students’ soft skills, such as logical thinking, communication, and attentiveness, assessed as part of the application system for those who applied to the University of Toronto’s Faculty of Applied Science and Engineering (FASE) undergraduate cohort in 2016, completed an Online Student Profile, and were admitted, and investigated the role of these skills in predicting further success and persistence in the program of study. The quantitative findings of this study revealed a weak positive and statistically significant association of students’ soft skills with first-semester sessional grade point average (SGPA) of first and second year and cumulative grade point average (CGPA). This association was not practically significant, however, and indicated a minimal predictive power of soft skills on SGPA and CGPA. The findings showed statistically significant differences in soft skills scores between groups based on background variables (e.g., gender and school type). The results from logistic regression of soft skills on time to graduation showed that students with higher logical thinking, written communication, communication average (oral and written), attentiveness, and total skills average are more likely to graduate within four years or, if they did a professional experience year, within five years. The qualitative findings, based on two interviews with each of nine participants representing a range of active and graduated students from different FASE cohorts and programs, revealed that participants viewed the transition to PSE as a complex and multifaceted process, with many different factors to negotiate. The findings indicated that participants entered the program with a solid foundation of soft skills acquired through various pre-postsecondary environments and experiences. Most participants had high efficacy beliefs, self-regulation, perseverance, logical thinking, and communication skills. The findings also demonstrated that participants were academically prepared. Transitioning into the program presented various difficulties for many participants. Nonetheless, they persisted by drawing on their soft and academic skills and adjusting their academic and career goals. Additionally, they were able to access various types of support.
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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 | 0,023 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».