Beyond Time-on-Task: The Relationship Between Spaced Study and Certification in MOOCs


  • Yohsuke Roy Miyamoto School of Engineering and Applied Sciences, Harvard University
  • Cody Coleman Massachusets Institute of Technology
  • Joseph Jay Williams HarvardX, Harvard University
  • Jacob Whitehill HarvardX, Harvard University
  • Sergiy Nesterko
  • Justin Reich HarvardX, Harvard University



MOOC, spacing effect, spaced practice, distributed practice, total time, sessions, online education


A long history of laboratory and field experiments has demonstrated that dividing study time into many sessions is often superior to massing study time into few sessions, a phenomenon widely known as the “spacing effect.” Massive open online courses (MOOCs) collect abundant data about student activity over time, but little of its early research has used learning theory to interrogate these data. Taking inspiration from this psychology literature, here we use data collected from MOOCs to identify observational evidence for the benefits of spaced practice in educational settings. We investigated tracking logs from 20 HarvardX courses to examine whether there was any relationship between how students allocated their participation and what performance they achieved.  While controlling for the effect of total time on-site, we show that the number of sessions students initiate is an important predictor of certification rate, across students in all courses. Furthermore, we demonstrate that when students spend similar amounts of time in multiple courses, they perform better in courses where that time is distributed among more sessions, suggesting the benefit of spaced practice independently of student characteristics. We conclude by proposing interventions to guide students’ study schedules and for leveraging such an effect.




How to Cite

Miyamoto, Y. R., Coleman, C., Williams, J. J., Whitehill, J., Nesterko, S., & Reich, J. (2015). Beyond Time-on-Task: The Relationship Between Spaced Study and Certification in MOOCs. Journal of Learning Analytics, 2(2), 47-69.



Special Section: Learning Analytics and Learning Theory