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EXAMINING CANADIAN GRADUATE STUDENTS' VIEWS ON IDEAL SUPERVISION: A QUALITATIVE CODING APPROACH.

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Title: EXAMINING CANADIAN GRADUATE STUDENTS' VIEWS ON IDEAL SUPERVISION: A QUALITATIVE CODING APPROACH.
Authors: JACOBSEN, MICHELE1, JOHNSTON, SONJA1, BUTLER, TANILLE1, PAQUETTE, ALEX1, BARAGAR, MICHELLE1
Source: Canadian Journal of Higher Education. 2025, Vol. 55 Issue 2, p94-113. 20p.
Subject Terms: *Graduate students, *Graduate education, *Qualitative research, Supervision, Mentoring
Geographic Terms: Canada
Abstract (English): Research highlights academic, mentoring, and personal characteristics students associate with ideal supervision. The Graduate Student Experience Survey (GSES) invited graduate students from all disciplines to share their views on the qualities and characteristics of ideal supervision. The quantity and diversity responses posed a challenge: How can we systematically analyze textual data from diverse graduate students across campus? In this article, we describe the creation and application of a qualitative coding framework--a systematic method for categorizing and coding textual data--to synthesize 824 student responses to an open-ended survey question. We administered the GSES in 2022 and 2023 and conducted a quantitative content analysis and qualitative interpretation of 993 data extracts. Findings are organized into five categories: personal characteristics, teaching/mentoring, relational trust, professional support, and academic support. This deductive approach to qualitative analysis enabled us to identify trends and patterns in the traits graduate students most frequently associated with ideal supervision. These findings have practical applications: researchers can adapt the qualitative coding framework to analyze textual data, graduate students can use findings to identify suitable supervisors, and university leadership can leverage findings to improve supervisory development. [ABSTRACT FROM AUTHOR]
Abstract (French): Cette recherche met en évidence diverses caractéristiques académiques, relatives au mentorat et personnelles que les étudiants associent à une supervision idéale. L'Enquête sur l'expérience des étudiants diplômés (GSES) a invité les étudiants à la maîtrise et au doctorat de toutes les disciplines à donner leur point de vue sur les qualités et les caractéristiques d'une supervision idéale. La quantité et la diversité des réponses ont posé un défi : comment analyser systématiquement les données textuelles des étudiants diplômés de l'ensemble du campus? Dans cet article, nous décrivons la création et l'application d'un cadre de codage qualitatif -- une méthode systématique de catégorisation et de codage des données textuelles -- pour synthétiser les réponses des étudiants à une question d'enquête ouverte. À l'aide de 993 extraits de 824 réponses au GSES obtenues en 2022 et 2023, nous avons réalisé une analyse quantitative du contenu et une interprétation qualitative en organisant les réponses en cinq catégories : caractéristiques personnelles, enseignement/mentorat, confiance relationnelle, soutien professionnel et soutien académique. Cette approche déductive de l'analyse qualitative nous a permis d'identifier des tendances et des modèles dans les traits que les étudiants diplômés associent le plus souvent à une supervision idéale. Ces résultats ont des applications pratiques : les chercheurs peuvent adapter le cadre de codage qualitatif pour analyser les données textuelles, les étudiants diplômés peuvent l'utiliser pour identifier les superviseurs appropriés, et les gestionnaires universitaires peuvent l'utiliser pour améliorer la supervision offerte. [ABSTRACT FROM AUTHOR]
Copyright of Canadian Journal of Higher Education is the property of Canadian Society for the Study of Higher Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Research highlights academic, mentoring, and personal characteristics students associate with ideal supervision. The Graduate Student Experience Survey (GSES) invited graduate students from all disciplines to share their views on the qualities and characteristics of ideal supervision. The quantity and diversity responses posed a challenge: How can we systematically analyze textual data from diverse graduate students across campus? In this article, we describe the creation and application of a qualitative coding framework--a systematic method for categorizing and coding textual data--to synthesize 824 student responses to an open-ended survey question. We administered the GSES in 2022 and 2023 and conducted a quantitative content analysis and qualitative interpretation of 993 data extracts. Findings are organized into five categories: personal characteristics, teaching/mentoring, relational trust, professional support, and academic support. This deductive approach to qualitative analysis enabled us to identify trends and patterns in the traits graduate students most frequently associated with ideal supervision. These findings have practical applications: researchers can adapt the qualitative coding framework to analyze textual data, graduate students can use findings to identify suitable supervisors, and university leadership can leverage findings to improve supervisory development. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (French)
  Group: Ab
  Data: Cette recherche met en évidence diverses caractéristiques académiques, relatives au mentorat et personnelles que les étudiants associent à une supervision idéale. L'Enquête sur l'expérience des étudiants diplômés (GSES) a invité les étudiants à la maîtrise et au doctorat de toutes les disciplines à donner leur point de vue sur les qualités et les caractéristiques d'une supervision idéale. La quantité et la diversité des réponses ont posé un défi : comment analyser systématiquement les données textuelles des étudiants diplômés de l'ensemble du campus? Dans cet article, nous décrivons la création et l'application d'un cadre de codage qualitatif -- une méthode systématique de catégorisation et de codage des données textuelles -- pour synthétiser les réponses des étudiants à une question d'enquête ouverte. À l'aide de 993 extraits de 824 réponses au GSES obtenues en 2022 et 2023, nous avons réalisé une analyse quantitative du contenu et une interprétation qualitative en organisant les réponses en cinq catégories : caractéristiques personnelles, enseignement/mentorat, confiance relationnelle, soutien professionnel et soutien académique. Cette approche déductive de l'analyse qualitative nous a permis d'identifier des tendances et des modèles dans les traits que les étudiants diplômés associent le plus souvent à une supervision idéale. Ces résultats ont des applications pratiques : les chercheurs peuvent adapter le cadre de codage qualitatif pour analyser les données textuelles, les étudiants diplômés peuvent l'utiliser pour identifier les superviseurs appropriés, et les gestionnaires universitaires peuvent l'utiliser pour améliorer la supervision offerte. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Canadian Journal of Higher Education is the property of Canadian Society for the Study of Higher Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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