Data-Driven Learner Profiling Based on Clustering Student Behaviors: Learning Consistency, Pace and Effort

Abstract

While it is important to individualize instruction, identifying and implementing the right intervention for individual students is too time-consuming for instructors to do manually in large classes. One approach to addressing this challenge is to identify groups of students who would benefit from the same intervention. As such, this work attempts to identify groups of students with similar academic and behavior characteristics who can benefit from the same intervention. In this paper, we study a group of 700 students who have been using ALEKS, a Web-based, adaptive assessment and learning system. We group these students into a set of clusters using six key characteristics, using their data from the first half of the semester, including their prior knowledge, number of assessments, average days and score increase between assessments, and how long after the start of the class the student begins to use ALEKS. We used mean-shift clustering to select a number of clusters, and k-mean clustering to identify distinct student profiles. Using this approach, we identified five distinct profiles within these students. We then analyze whether these profiles differ in terms of students’ eventual degree of content mastery. These profiles have the potential to enable institutions and instructors using ALEKS to identify students in need and devise and implement appropriate interventions for groups of students with similar characteristics and needs.