Effects of Teachers Acting Naturally vs. Didactically During Video Learning

  • Selina Emhardt (Speaker)

Activity: Talk or presentation typesTalk or presentation (not at a conference)Academic


Video modeling examples (VME), in which a teacher (or ‘model’) demonstrates how to perform a task, have recently become a popular tool for online education. A way to further enhance these videos is by using eye-tracking technology to visualize the location of the model’s eye movements during task performance, for instance as a superimposed moving dot. Such Eye Movement Modeling Examples (EMME) have often been found to be beneficial for learning. However, the behavior of the models varies substantially across EMME, and we do not yet know whether and how this affects students’ learning. In this study, we investigated the effects of displaying a model’s natural or more didactic problem-solving behavior in EMME. We defined natural behavior as the model’s regular approach to solve a code debugging problem. We defined behavior as didactic when the model aimed to explain how to solve the same task in an understandable manner to a novice audience. Sixty-four participants watched an EMME that either showed a programming teacher’s natural or didactic debugging behavior. Unexpectedly, the displayed model behavior did not affect learners’ perceived mental effort and post-test performance. We conclude that the effectiveness of the EMME videos is not affected by the model’s type of behavior, provided that the model is an experienced teacher. Future studies should extend this research to other video types and models with less teaching experience. Investigating the effects of different video characteristics on learning can ultimately help to create evidence-based guidelines to design effective instructional videos.
Period27 Aug 2021
Event titleEARLI 2021
Event typeConference
Conference number19
LocationGothenburg, SwedenShow on map
Degree of RecognitionInternational


  • educational technology
  • instructional design
  • higher education
  • computer-assisted learning
  • eye tracking