Using Learning Analytics to Trace Academic Trajectories of CS and IT Students to Better Understanding Successful Pathways to Graduation (Abstract Only)

Abstract

We present findings from a study examining students' course-taking pathways to graduation and identify the factors regarding course-taking choices that can affect students' performance. The data for the study was collected from two majors within an engineering school at a large public university: computer science (CS) and information technology (IT). Although we look in depth at CS students, we use the other data for a comparative analysis. CS and IT are the most popular and largest majors, respectively, at our institution with almost 2,500 undergraduate students enrolled in them. The results show that there are differences in specific patterns of courses and illustrate relationships between the frequent courses in each semester and the relationships between courses taken in two consecutive semesters. Some major insights from the analysis of trajectory of frequent courses for both groups include: low performers postponed some courses toward the end of the program, and take a collection of courses together that their counterparts do not usually take. This work has direct implications for advising of prospective and current students and can improve programs' curriculum and students' performance. In the next stage of this study we will compare trajectories of students who graduate with those of students who either leave CS and IT or take longer to graduate. This preliminary research is part of a NSF CISE/EHR funded grant project on BigData and Education.