Date
5-2019
Department
School of Education
Degree
Doctor of Education in Curriculum & Instruction (EdD)
Chair
Michael Shenkle
Keywords
Digital Textbooks, Learning Analytics, Student Success
Disciplines
Education | Higher Education | Online and Distance Education
Recommended Citation
Williams, Dustin Lee, "Predicting Student Success Using Digital Textbook Analytics in Online Courses" (2019). Doctoral Dissertations and Projects. 2084.
https://digitalcommons.liberty.edu/doctoral/2084
Abstract
In the digital era, students are generating and institutions are collecting more data than ever before. With the constant change in technology, new data points are being created. Digital textbooks are becoming more popular, and textbook publishers are shifting more of their efforts to creating digital content. This shift creates new data points that have the potential to show how students are engaging with course material. The purpose of this correlational study is to determine if digital textbook usage data, pages read, number of days, reading sessions, highlights, bookmarks, notes, searches, downloads and prints can predict student success. This study used a multiple regression to determine if digital textbook usage data is a predictor of course or quiz success in five online undergraduate courses at a private liberal arts university. The analysis used digital textbook data from VitalSource and consisted of 1,602 students that were enrolled in an eight-week online course at a private liberal arts university. The analysis showed that there is a significant relationship between digital textbook usage data and total points earned and average quiz grade. This study contributes to the limited knowledge on digital textbook analytics and provides valuable insight into how students engage with digital textbooks in online courses.
