JOURNAL ARTICLE
Contributing Factors for Success of Nontraditional Students at Online Doctoral Programs.
Published In: Journal of College Student Retention: Research, Theory & Practice, 2025, v. 27, n. 1. P. 26 1 of 3
Database: Education Source Ultimate 2 of 3
Authored By: Kebritchi, Mansureh; Rominger, Ryan; McCaslin, Mark 3 of 3
Abstract
This study investigates the nature of student success and contributing factors for nontraditional students in online doctoral programs through a mixed-methods approach involving doctoral alumni, faculty, and administrators. Student success is primarily defined as degree completion and professional advancement, supported by personal qualities such as grit, effective faculty relationships, skill acquisition, and social support. Quantitative analysis revealed that higher levels of student grit, but not Big Five personality traits, correlate with shorter time to degree completion, while program alignment with industry standards also facilitates timely graduation and employment in the field. Faculty roles as mentors and guides, along with a supportive and flexible program structure, are emphasized as critical external factors enhancing success. The study expands existing online education and achievement goal models by integrating grit and program-student goal alignment, offering insights for improving retention and outcomes in online doctoral education for adult learners.
Additional Information
- Source:Journal of College Student Retention: Research, Theory & Practice. 2025/05, Vol. 27, Issue 1, p26
- Document Type:Article
- Subject Area:Education
- Publication Date:2025
- ISSN:15210251
- DOI:10.1177/15210251231155488
- Accession Number:184233628
- Copyright Statement:Copyright of Journal of College Student Retention: Research, Theory & Practice is the property of Sage Publications Inc. 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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