Too Anxious to Talk: Social Anxiety, Communication, and Academic Experiences in Higher Education
Notice bibliographique
Résumé
The first overarching goal of this doctoral dissertation was to develop and measure a new construct termed academic communication.Accordingly, Study 1 focussed on item development, pilot testing, and examining the psychometric properties of the newly developed Academic Communication Inventory (ACI).Undergraduate students (N = 642, Mage = 19.5) completed the ACI (assessing general communicative behaviours) along with other measures to investigate external validity.Results demonstrated that the best fitting structure of the ACI was a two-factor solution, consisting of the subscales: (1) communication with instructors; and (2) communication with peers.Study 2 assessed measure invariance across educational context (i.e., blended courses, online courses, offline courses), as well as gender differences in communication.Participants were undergraduate students (N = 1074, Mage = 20.3)who completed the ACI (assessing course-specific communicative behaviours), with 21% subset completing follow-up questionnaires (participants from Study 2 were also used in Studies 3 and 4 for different research purposes).Multi-group factor analyses suggested that the ACI could be used as both a general and course-specific measure of academic communication (i.e., the ACI was invariant across course contexts).Moreover, females and males reported different communication levels with instructors and peers.Study 3 focused on the utility of the ACI, by examining the links between social anxiety, communication, academic experiences (i.e., engagement, classroom connectedness, student satisfaction) and wellbeing.Among the results, academic communication accounted for significant variance in the links between social anxiety and academic experiences.Moreover, social anxiety was negatively related to academic experiences, and there was at least some conversations about academia, research, and life in general.Rob, you never failed to provide advice that helped guide me through the throes of graduate school.Your wisdom, patience, encouragement, and unconditional support made this all possible.I am so lucky to call you my mentor and friend.I would like to thank the Coplan lab (past and present), for being there for brainstorming sessions, to vent frustrations, and for all the games nights.You helped to create a safe and fun space during a time of immense stress and pressure.A special shout out to Laura -I won't ever forget the hundreds of hours spent on the phone (you're welcome, Rogers), the emotional support you provided, your problem-solving abilities, and conferences.To my family, last but definitely not least.Thank you for always having faith in my abilities, and for always boosting my spirits when I needed it.Mom and Dad, throughout my life, you provided me with the tools I needed to excel.I would not be where I am without your unwavering support and encouragement.To my husband, Jason -thank you for encouraging me to move to Ottawa so many years ago, so I could pursue my MA and PhD with Rob.Even though we were not always in the same city, you were always there to support my aspirations, provide reassurance, and make me laugh.I am very fortunate to have a partner like you to experience life with.Finally, to my little Brielle -your presence gave me the final push I needed to finish my dissertation.And for that, along with all the joy and love you bring into my life, I will forever be grateful.
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 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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 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 ».