The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

• Published in: International Journal of Educational Technology in Higher Education Vol. 17; no. 1; pp. 1 - 24
• Main Authors: Baker, Rachel, Xu, Di, Park, Jihyun, Yu, Renzhe, Li, Qiujie, Cung, Bianca, Fischer, Christian, Rodriguez, Fernando, Warschauer, Mark, Smyth, Padhraic
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 14.04.2020; Springer Nature B.V; SpringerOpen
• Subjects: Academic Achievement; Basic Skills; CAI; Classrooms; Clickstream data; Computer Appl. in Social and Behavioral Sciences; Computer assisted instruction; Computer Science; Computers and Education; Course Content; Descriptive analysis; Distance Education; Distance learning; Educational Research; Educational Technology; Electronic Learning; Environmental Influences; Higher Education; Humanities; Independent study; Influence of Technology; Information Systems Applications (incl.Internet); Instructional Design; Interpersonal Relationship; Intervention; Law; Learning Experience; Learning Management Systems; Learning Processes; Learning Strategies; Online Courses; Online education; Online instruction; Outcomes of Education; Protocol Analysis; Psychological Patterns; Questionnaires; Review Article; Statistics for Social Sciences; Student Behavior; Student Surveys; Teachers; Time Management; Intervention; Descriptive analysis; Clickstream data; Online education

Student clickstream data—time-stamped records of click events in online courses—can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resou...