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The Cohort Experience in a Hybrid Instructional Technology Ph.D. Program.

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Title: The Cohort Experience in a Hybrid Instructional Technology Ph.D. Program.
Authors: Rice, Margaret L.1 (AUTHOR) mrice@ua.edu, Beavers, Bernadette1 (AUTHOR), McNeill, Laura1 (AUTHOR), Benson, Angela1 (AUTHOR)
Source: TechTrends: Linking Research & Practice to Improve Learning. Jul2022, Vol. 66 Issue 4, p710-720. 11p.
Subject Terms: *Educational technology, *Doctoral programs, *Community of inquiry, *Data analysis, Technology transfer, Thematic analysis
Abstract: The purpose of this qualitative study was to gain in-depth understanding of students' experiences in a cohort-based hybrid instructional technology Ph.D. program at a Research 1 University. Participants were 14 current and former students in the Instructional Technology Ph.D. program from the 2016 through 2020 cohorts. Participants completed an eight-question open-ended survey administered using Qualtrics. The survey was developed using the Community of Inquiry (CoI), which also serves as the theoretical framework for this study. Thematic analysis was used to analyze the data. Four themes related to cohort success emerged from the analysis of the data. Results provide faculty and administrators with a better understanding of how students experience a cohort-based hybrid Ph.D. program, including the fundamental support students need for personal and academic success. Themes revealed through this research will inform faculty and administrators of critical elements to consider in the design and development of future cohort-based doctoral programs. [ABSTRACT FROM AUTHOR]
Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature 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.)
Database: Education Research Complete
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  Data: *<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Doctoral+programs%22">Doctoral programs</searchLink><br />*<searchLink fieldCode="DE" term="%22Community+of+inquiry%22">Community of inquiry</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+transfer%22">Technology transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Thematic+analysis%22">Thematic analysis</searchLink>
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  Data: The purpose of this qualitative study was to gain in-depth understanding of students' experiences in a cohort-based hybrid instructional technology Ph.D. program at a Research 1 University. Participants were 14 current and former students in the Instructional Technology Ph.D. program from the 2016 through 2020 cohorts. Participants completed an eight-question open-ended survey administered using Qualtrics. The survey was developed using the Community of Inquiry (CoI), which also serves as the theoretical framework for this study. Thematic analysis was used to analyze the data. Four themes related to cohort success emerged from the analysis of the data. Results provide faculty and administrators with a better understanding of how students experience a cohort-based hybrid Ph.D. program, including the fundamental support students need for personal and academic success. Themes revealed through this research will inform faculty and administrators of critical elements to consider in the design and development of future cohort-based doctoral programs. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature 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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              Text: Jul2022
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