Showing a model's eye movements in examples does not improve learning of problem-solving tasks
Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention. EMME have been shown to enhance learning of perceptual classification tasks; however, it is an open question whether EMME would also improve learning of procedural problem-solving tasks. We investigated this question in two experiments. In Experiment 1 (72 university students, Mage = 19.94), the effectiveness of EMME for learning simple geometry problems was addressed, in which the eye movements cued the underlying principle for calculating an angle. The only significant difference between the EMME and a no eye movement control condition was that participants in the EMME condition required less time for solving the transfer test problems. In Experiment 2 (68 university students, Mage = 21.12), we investigated the effectiveness of EMME for more complex geometry problems. Again, we found no significant effects on performance except for time spent on transfer test problems, although it was now in the opposite direction: participants who had studied EMME took longer to solve those items. These findings suggest that EMME may not be more effective than regular video examples for teaching procedural problem-solving skills.
|Keywords||Attention cueing, Example-based learning, Eye tracking, Multimedia learning|
|Persistent URL||dx.doi.org/10.1016/j.chb.2016.08.041, hdl.handle.net/1765/93903|
|Journal||Computers in Human Behavior|
van Marlen, T.V.A, van Wermeskerken, M, Jarodzka, H, & van Gog, T.A.J.M. (2016). Showing a model's eye movements in examples does not improve learning of problem-solving tasks. Computers in Human Behavior, 65, 448–459. doi:10.1016/j.chb.2016.08.041