Preview Analytics of ePUB3 eBook-based Flipped Classes Using a Big Data Approach

Abstract

Flipped learning has been commonly used to provide students with learning contents inside/outside classrooms. It encourages students to preview learning contents before their classes. Thereafter, learning activities are taken in classes with instructions or help from the teacher. An important issue in the success of flipped learning is the effectiveness of students’ preview because it affects the subsequent learning activities in their classes. Fortunately, for ePUB3 eBooks used in flipped classes, the embedded track and test functions can be used to track the accesses and tests taken on these eBooks. As such, the teacher can capture the effectiveness of students’ preview by checking their pre-class accesses and tests, and hence take adequate actions for subsequent class activities. In this paper, we present an analytics approach investigating the effectiveness of students’ preview. Their accesses and tests on ePUB3 eBooks are tracked, recorded, and analysed using a big data BOOCs (eBook Open Online Courses) platform. The approach is applied to a 3-class course at a university in Taiwan with 71 first-year students enrolled. As the results illustrate, the approach is useful for supporting teachers to take adequate actions in classes. Students’ preview effectiveness is gradually enhanced in later classes.