Digital learning materials are on the rise in higher education settings (Treve, 2021; Zawacki-Richter, 2021). This implies a growing need for research on how they should be designed to assist learning effectively (Sailer & Schlag, 2012). Therefore, our project aims at exploring whether empirical behavioral methods can provide a more valid insight into realistic learning processes. Furthermore, we will try to identify facilitating factors for learning with digital learning materials. One factor under consideration are emotions since they have proven to be influential factors on learning (Pekrun et al. 2017; Tnyg et al. 2017).
Our current study investigates how eye tracking and analysis of emotional facial expressions might be used to assess differences in learning behavior in applied educational settings. Regarding emotions, we hypothesize that coded emotional face expressions will differ between types of information presentation (i.e. text, figures or videos).
70 students were observed in a laboratory setting while working through authentic digital learning material. Eye movements and portrait face videos were recorded. Self-report data on positive and negative affects while learning and motivational factors of technology implementation (Gorovoj, 2019) were collected. For data analysis automated coding of facial emotion expressions will be combined with eye movement cues on the focus of the participants attention. Data analyses are still in progress.
We would like to discuss potentials and limitations of introducing behavioral measures to research in higher education.