Doctoral Students’ Learning Success in Online-Based Leadership Programs: Intersection With Technological and Relational Factors

Authors

  • HyunKyung Lee Hankuk University of Foreign Studies
  • Heewon Chang
  • Lynette Bryan

DOI:

https://doi.org/10.19173/irrodl.v20i5.4462

Keywords:

online education, online learning success, leadership doctoral program, technological factors, relational factors

Abstract

This study examines how technological and relational factors independently and interactively predict the perceived learning success of doctoral students enrolled in online-based leadership programs offered in the United States. The 73-item Online Learning Success Scale (OLSS) was constructed, based on existing instruments, and administered online to collect self-reported data on three primary variables: student learning success (SLS), relational factors (RF), and technological factors (TF). The SLS variable focuses on the gain of knowledge and skills, persistence, and self-efficacy; the RF on the student-student relationship, the student-faculty relationship, and the student-non-teaching staff relationship; and the TF on the ease of use, flexibility, and usefulness. In total, 210 student responses from 26 online-based leadership doctoral programs in the United States were used in the final analysis. The results demonstrate that RF and TF separately and together predict SLS. A multiple regression analysis indicates that, while all dimensions of TF and RF are significant predictors of SLS, the strongest predictor of SLS is the student-faculty relationship. This study suggests that building relationships with faculty and peers is critical to leadership doctoral students’ learning success, even in online-based programs that offer effective technological support.

Published

2020-01-01

How to Cite

Lee, H., Chang, H., & Bryan, L. (2020). Doctoral Students’ Learning Success in Online-Based Leadership Programs: Intersection With Technological and Relational Factors. The International Review of Research in Open and Distributed Learning, 21(1), 61–81. https://doi.org/10.19173/irrodl.v20i5.4462

Issue

Section

Research Articles