Classifying and visualizing students' cognitive engagement in course readings.

Eran Yogev, Kobi Gal, David R. Karger, Marc T. Facciotti, Michele Igo

Research output: Contribution to conferencePaperpeer-review


Reading material has been part of course teaching for centuries, but until recently students' engagement with that reading, and its effect on their learning, has been difficult for teachers to assess. In this article, we explore the idea of examining cognitive engagement---a measure of how deeply a student is thinking about course material, which has been shown to correlate with learning gains---as it varies over different sections of the course reading material. We show that a combination of automatic classification and visualization of cognitive engagement anchored in the text can give teachers---and not only researchers---valuable insight into their students' thinking, suggesting ways to modify their lectures and their course readings to improve learning. We demonstrate this approach with analyzing students' comments in two different courses (Physics and Biology) using the Nota Bene annotation platform.
Original languageEnglish
Number of pages10
StatePublished - 28 Jun 2018
Event5th Annual ACM Conference on Learning at Scale, L at S 2018 - London, United Kingdom
Duration: 26 Jun 201828 Jun 2018


Conference5th Annual ACM Conference on Learning at Scale, L at S 2018
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Education
  • Software
  • Computer Science Applications


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