Students from a large Australian university use Twitter to identify difficult course concepts to review during face-to-face lectorial sessions.

Academic Article


  • Engaging undergraduate students in large classes is a constant challenge for many lecturers, as student participation and engagement can be limited. This is a concern since there is a positive correlation between increased engagement and student success. The lack of student feedback on content delivery prevents lecturers from identifying topics that would benefit students if reviewed. Implementing novel methods to engage the students in course content and create ways by which they can inform the lecturer of the difficult concepts is needed to increase student success. In the present study, we investigated the use of Twitter as a scalable approach to enhance engagement with course content and peer-to-peer interaction in a large course. In this pilot study, students were instructed to tweet the difficult concepts identified from content delivered by videos. A software program automatically collected and parsed the tweets to extract summary statistics on the most common difficult concepts, and the lecturer used the information to prepare face-to-face (F2F) lectorial sessions. The key findings of the study were 1) the uptake of Twitter (i.e., registration on the platform) was similar to the proportion of students who participated in F2F lectorials, 2) students reviewed content soon after delivery to tweet difficult concepts to lecturer, 3) Twitter increased engagement with lecturers, 4) the difficult concepts were similar to previous years, yet the automated gathering of Twitter data was more efficient and time saving for the lecturer, and 5) students found the lectorial review sessions very valuable.
  • Authors


    Publication Date

  • January 1, 2021
  • Keywords

  • Australia
  • Humans
  • Pilot Projects
  • Social Media
  • Students
  • Twitter discussions
  • Universities
  • flipped teaching
  • large lecture
  • lectorials
  • student engagement
  • Digital Object Identifier (doi)

    Pubmed Id

  • 33439783
  • Start Page

  • 10
  • End Page

  • 17
  • Volume

  • 45
  • Issue

  • 1