Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses


  • Jeremy Riel University of Illinois at Chicago
  • Kimberly A. Lawless University of Illinois at Chicago
  • Scott W. Brown University of Connecticut




Timing, participation, engagement, repetition, online learning, distance education, informal learning, self-paced learning, professional development, procrastination, spacing effect.


One feature of self-paced online courses is greater learner control over the timing of their work in a course. However, the greater timing flexibility that learners enjoy in such environments may play a different role in the learning process than has been previously observed in formal online or face-to-face courses. As such, the study of work timing merits further investigation. Toward this goal, this study forwards two measures that represent the timing of coursework: 1) the timing index, or the degree to which a participant completes 50% of their work, and 2) the spacing count, the frequency of work performed across the course timeframe. In this study, the authors demonstrate the use of these measures from a data set of 42 U.S. middle-school teachers who participated in a self-paced, online professional development course to support teacher implementation of a new blended-learning curriculum. Using the two measures, the authors identify timing behaviours of participants and examine the effects that timing has on teacher self-efficacy after completing the course. The two measures and visualizations demonstrated in this paper yield useful individual-level variables for course timing that can be used for further study on the effects on learning outcomes.


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How to Cite

Riel, J., Lawless, K. A., & Brown, S. W. (2018). Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses. Journal of Learning Analytics, 5(1), 25–40. https://doi.org/10.18608/jla.2018.51.3



Special Section: It's About Time: Temporal Analysis of Learning Data Part 2