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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
Language: English
Authors: Michele Jacobsen, Sonja Johnston, Tanille Butler, Alex Paquette, Michelle Baragar
Source: Canadian Journal of Higher Education. 2025 55(2):94-113.
Availability: Canadian Society for the Study of Higher Education. P.O. Box 34091, RPO Fort Richmond, Winnipeg, MB R3T 5T5, Canada. Tel: 204-474-6404; Fax: 204-474-7561; e-mail: csshe@cc.umanitoba.ca; Web site: http://journals.sfu.ca/cjhe/index.php/cjhe/index
Peer Reviewed: Y
Page Count: 20
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Graduate Students, Supervision, Individual Characteristics, Mentors, Student Attitudes, Trust (Psychology), Supervisor Supervisee Relationship, Role Perception
Geographic Terms: Canada
ISSN: 0316-1218
Abstract: 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1480244
Database: ERIC
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  Data: Canadian Society for the Study of Higher Education. P.O. Box 34091, RPO Fort Richmond, Winnipeg, MB R3T 5T5, Canada. Tel: 204-474-6404; Fax: 204-474-7561; e-mail: csshe@cc.umanitoba.ca; Web site: http://journals.sfu.ca/cjhe/index.php/cjhe/index
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  Data: <searchLink fieldCode="DE" term="%22Canada%22">Canada</searchLink>
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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.
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Graduate Students
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      – SubjectFull: Supervision
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      – SubjectFull: Student Attitudes
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      – SubjectFull: Canada
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