How students interact with your online course predicts their success

We all know that students are more successful when they have a deep interest in course materials or strong thinking skills.  Similarly, we know we can help students do better when assessment is transparent. However, we are less aware of the impact that our course design has on likely student persistence and success. In online and remote environments, here are some key predictors of success controlled by how you design your course:

Key factors:

  1. Student engagement with learning activities like posting on discussion boards or taking optional online quizzes to check their understanding (Zacharis, 2015) is a  likely predictor of student success.  Interestingly, time logged in and reading or viewing is only weakly correlated with success.
  2. Students completing small quizzes and optional self-assessments are also predictive of later success in the course (Macfadyen & Dawson, 2010).  When student synthesize and self-check for understanding, it reduces the likelihood that they will continue to overestimate what they actually understood from the reading or self-assessment. The small and optional items, like practice test, also decrease instances of cheating.
  3. Active learning strategies such as highlighting and annotating the e-textbook content actually have a significant correlation with a student’s final grade (Junco & Clem, 2015), especially if students do it in combination with other students.

What to do:

  1. Build active learning into your course, because all student effort with your content is not equal.  When they are making sense for themselves or self-assessing, they are learning more. Discussion boards, self-assessment, synchronous conversation with others, and the combination of short video and a quick check for understanding (not graded but automatically marked by the LMS) are all examples of essential elements beyond sharing your content.
  2. When you do need to share content, pair it with something active like social annotating.  USask has access to Perusal, which embeds directly into Canvas, for example and allows students to annotate their reading together.
  3. Use your analytics in Canvas, and have system automatically connect with students who are delaying accessing materials, especially activities. Accessing late and avoiding active components are highly predictive of failure, but even when students know a message is automatic, a substantial number of students will still improve their learning behaviors after a reminder to engage.  Want to learn more about your students? Use Know Your Class to learn their year of study and other demographics.

Read more:

  • If you love a good quantitively designed study: Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. Internet and Higher Education, 27, 44-53.
  • On using analytics to predict success Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599. https://doi.org/ 10.1016/j.compedu.2009.09.008.
  • On the role of your digital textbook Junco, R., & Clem, C. (2015). Predicting course outcomes with digital textbook usage data. The Internet and Higher Education 27, 54-63. https:/ /doi.org/10.1016/j.iheduc.2015.06.001