Western Illinois University
Machine data as the source of learning engagement in hands-on learning online
Instructional technology provides the capacity to address the needs of students with diverse cognitive skills and socialization needs. Learning experience is viewed as an important factor in learner engagement/motivation, and a contributor to learning in online instruction (Sims, 2003; Swartzwelder & Murphy, 2019; Chan, Wan & Ko, 2019). Moore’s three types of learning interaction (Moore, 1989) included student-content interaction, student-student interaction, and student-faculty interaction; and have been used widely in the research literature. Several studies have demonstrated that well-designed online interactivities can improve student’s learning experience (Svihla, 2015; Cain & Lee, 2016; Watkins, 2005; Herrington, Oliver & Reeves, 2003). However, the field has no clear agreement on how to measure these interactivities for improving learning experience in online instruction (Ekwunife-Orakwue and Teng, 2014; Walmsley-Smith, Machin and Walton, 2019). Some assume that an analytics approach, using tracking data from behavioral and physiological responses (e.g., facial expressions, eye tracking, click-stream data) as evidence of involvement and attentiveness, is a measure of motivation and engagement. Using the physiological response data in online instruction can be a reliable source of understanding online activities that enhance learning experience (Lee & Shapiro, 2019; Lee & DuMont, 2010). The purpose of this project is to explore how to design learning activities in hands-on lessons online that are effective and engaging based on facial expressions and physiological responses.
This project designed four popular types of learning activities, video, simulation, drill & practice, and concept map in the engineering and technology field. The result example demonstrated a female student’s learning engagement when she experienced different online activities with the physiological responses and facial expressions from iMotions. This project found that student has different emotional responses during the different types of learning activities.
This study employed both Facial Expression Analysis and Galvanic Skin Response. To learn more about how these two biosensors can improve and help your research goals simply follow the links. If you are also interested in learning more about human behavior in general, you can download our dedicated pocket guide on the subject below: