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Cited by 52Doctoral Education Challenges and SolutionsOnline and Blended LearningHigher Education Research Studies

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A Preliminary Examination of Doctoral Student Retention Factors in Private Online Workspaces

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(Cathy) Ames, Cathrine, Berman, Ronald, Casteel, Alex·2018·International Journal of Doctoral Studies
DOI 10.28945/3958
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Abstract

Aim/Purpose: The purpose of this quantitative descriptive study is to provide a preliminary examination of students’ retention factors of engagement, communication, and isolation that may be affected by the introduction and use of online communities for dissertation development within an online doctoral program. Background: This research is a continuation of the university’s 5-year research initiative to address the high national rate of doctoral attrition by investigating whether private online workspaces provide a virtual platform to increase student interaction, enhance student communication, and reduce student perception of isolation. Methodology: A quantitative descriptive study of 698 doctoral students (n1 = 355, n2 = 179, n3 = 184) in the online environment across three survey periods over a span of 30 months. Contribution: In 30 months, student engagement increased, perceptions of effective communication by students with dissertation committees improved, and student perceptions of isolation remained unchanged. Findings: The implementation of online workspaces for doctoral students addressed factors experienced in online doctoral programs. The introduction of private doctoral workspaces significantly improved doctoral students’ perceptions of more effective communication with their dissertation committees. Perceptions of isolation remained unchanged with the introduction of the technology. Recommendations for Practitioners: Universities and faculty should make proactive efforts to utilize the online tools available to them to facilitate improved communication and reduce isolation within online doctoral programs. Recommendation for Researchers: The implementation of online workspaces appears to mitigate some factors associated with student attrition, but the extent of these changes is unknown. Future research should continue to examine the factors of retention as a pathway to reducing attrition within the online learning environment. Impact on Society: The implementation of private online workspaces appears to lessen factors associated with student attrition, providing opportunities for improved utilization of personal and university resources, improved professional standing for graduates, and an enhanced reputation for online learning programs. Future Research: Further examination is needed to determine to what extent various communication methods affect a student’s experience and increase connectivity between the student and the institution, as well as research to better understand the phenomenon of students’ perceptions of isolation within online environments.

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Cited by

Metadata

Authors
(Cathy) Ames, C., Berman, R., & Casteel, A.
Affiliations
  • Grand Canyon University
  • Grand Canyon University (US)
ORCID
Casteel, Alex
Venue
International Journal of Doctoral Studies
Publisher
Informing Science Institute
Published
2018
Volume
13
Pages
079-107
ISSN
  • 1556-8865 (ISSN-L)
  • 1556-8881
  • 1556-8873
DOI
10.28945/3958
OpenAlex
W2942510847
Cited by
52
References
1
Citations by year
2025: 4 · 2024: 7 · 2023: 8 · 2022: 3 · 2021: 15 · 2020: 5 · 2019: 7 · 2018: 3
FWCI
11.50
Type
journal article
Language
en
Open access
Licence
Locations
International journal of doctoral studies (publishedVersion, cc-by-nc)
Topics
  • Social Sciences › Education › Doctoral Education Challenges and Solutions
  • Social Sciences › Education › Online and Blended Learning
  • Social Sciences › Education › Higher Education Research Studies
Keywords
doctoral student retention, doctoral attrition, online doctoral programs, student engagement, student isolation, online communities

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.