Date of Final Oral Examination (Defense)
Type of Culminating Activity
Doctor of Education in Educational Technology
Jui-Long (Andy) Hung, Ph.D.
Kerry Lynn Rice, Ph.D.
Jesus Trespalacios, Ph.D.
This learning analytics study looked at the various student characteristics of all on-campus students who were enrolled in 100 and 200 level courses that were offered in both online and face-to-face formats during a two-year period. There is a perception that online education is either not as successful as face-to-face instruction, or it is more difficult for students. The results of this study show this is not the case.
The goal of this study was to complete an in-depth analysis of student profiles addressing a variety of demographic categories as well as several academic and course related variables to reveal any patterns for student success in either online or face-to-face courses as measured by final grade. There were large enough differences within different demographic and academic categories to be considered significant for the study population, but overwhelmingly, the most significant predictor of success was found to be past educational success, as reflected in a student’s cumulative grade point average.
Further analysis was completed on students who declared high school credit as their primary major based on significantly different levels of success. These students were concurrent enrollment students or those who completed college courses for both high school and university credit. Since most of these students were new to the university, they did not have a cumulative GPA, so other predictive factors were explored. The study concludes with recommendations for action based on the logistic regression prediction tool that resulted from the data analysis.
Berry, Lisa Janine, "Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments" (2017). Boise State University Theses and Dissertations. 1244.