Using Educational Data Mining to Identify and Analyze Student Learning Strategies in an Online Flipped Classroom

Abstract

Analyzing the learning analytics from a course provides insights that can impact instructional design decisions. This study used educational data mining techniques, specifically a longitudinal k-means cluster analysis, to identify the strategies students used when completing the online portion of an online flipped spreadsheet course. An analysis of these results showed that students did tend to follow a specific learning strategy as they completed this course. However, students also self-regulated to some degree, based on the topic and context of specific lessons. These insights not only improve our understanding about the students taking the course, but they also provide guidance for how the instructional design of the course might be improved. Of note is the practical value of this proof-of-concept study in using educational data mining to improve the instructional design of a course.