The Affect Effect: Integrating Student Emotions into the Design of Engineering Technology Courses with Optimization Method

摘要

This Innovative Practice Full Paper presents a study that enhanced both cognitive and non-cognitive learning outcomes through optimizing science, technology, engineering and math (STEM) students’ affective responses. Much of engineering education has focused on designing courses and curriculum to maximize both cognitive and, increasingly, non-cognitive learning outcomes. The role of affective outcomes, such as feelings or values, have been comparatively under-studied in the engineering context [1]. This despite the fact that there is promising research in both educational psychology and computer science that links positive affect with enhanced learning outcomes [2], [3].To design a study based on affect, an engineering professor partnered with an advanced undergraduate psychology student to develop a model for capturing and applying affective outcomes in applied engineering courses. To measure affect, we collected weekly surveys of the students’ affective responses to both the mode of delivery and nature of the content using categories such as boredom, surprise, and confidence, each of which have been identified as potentially significant by other researchers. We then integrated the students’ responses into a predictive linear recursion model, which was, in turn, used to make curricular decisions periodically throughout the semester. In other words, the students’ affective responses were used to influence the content and the delivery of the course as it was being taught.Our findings suggest that using this predictive model to optimize affective responses could be used as the basis of responsive course design and the enhancement of student learning outcomes. The study has implications for the further study of the role of affective outcomes in engineering education; as well as the advancement of co-created (with students) models of instructional and curricular design that incorporate affective variables.