Date
8-6-2025
Department
School of Education
Degree
Doctor of Philosophy in Education (PhD)
Chair
Jillian L. Wendt
Keywords
educational technology, online learning, student engagement, cognitive load, technology self-efficacy
Disciplines
Higher Education | Online and Distance Education
Recommended Citation
Ozuzu, Chikezie Ebubechukwu, "A Predictive Correlational Study of the Number of Edtech Tools in Graduate Online Courses and Student Gender on Student Engagement" (2025). Doctoral Dissertations and Projects. 7295.
https://digitalcommons.liberty.edu/doctoral/7295
Abstract
The purpose of this quantitative predictive correlational design study was to examine if and to what extent the number of educational technology (edtech) tools in a course and student gender could predict engagement in online courses among New York City graduate education students. As higher education institutions continue to integrate diverse technologies into their online learning environments, understanding how the use of many tools influences student engagement is important for instructional design, policy, and pedagogy. The participants for this study included a sample of 142 students drawn from the population of graduate students enrolled in different online sections of the same online course offered at a graduate school of education in New York City. The setting for this study was the graduate online course sections. Data was collected using the Motivation and Engagement Scale—University/College (MES–UC). The digital version of the instrument was offered via an email link to the participants as part of the end-of-course activities. Data collected was analyzed using multiple linear regression after initial data screening for errors, missing entries, and inconsistencies. The results revealed that neither the number of edtech tools nor student gender significantly predicted engagement. The overall regression model explained less than 1% of the variance in student engagement (R2 = .003, F (2, 139) = .193, p = .825). The findings suggest that the quantity of edtech tools and student gender alone may not be sufficient predictors of engagement in graduate online courses. Future research should explore additional variables such as instructional quality, student-student and instructor-student interactions, technology self-efficacy, and cognitive load. Longitudinal, experimental, or mixed methods studies are recommended to provide deeper insights into student engagement.
