EXPLORING STUDENT ENGAGEMENT IN LEARNING CLIMATE SCIENCE IN DIFFERENT INSTRUCTIONAL SETTINGS

Teaching climate science poses many challenges to instructors and teachers, such as its scientific complexity, the interdisciplinarity of the topic as well as the need to disentangle cultural worldviews and political dimensions surrounding the topic. Identifying effective instructional strategies and approaches for teaching about climate is critical in order to increase learning outcomes and to develop effective learning environments and materials. While increased engagement is linked to increased learning gains, it has been difficult to measure engagement around climate science. Galvanic skin sensors are innovative and valid tools for measuring student engagement in controlled settings, but skin sensors are just starting to be used in authentic educational contexts. Using newly developed open-source analysis tools specifically for skin sensor data, here we analyze and compare biometric (skin sensors), behavioral data (observation protocols) and self-reflection data from an introductory large-group undergraduate climate science course, a small-group controlled study environment and a teacher workshop. We ask the following questions: 1) How does the engagement of learners in a climate science class vary with instructional method and content? 2) Can certain icons, topics or content serve as an engagement hook for learning? Based on skin sensor results from a small-group, controlled learning setting, students are most engaged during one-on-one dialogue activities. We find that in large-group undergraduate classrooms the perceived (peer pressure, equations) or actual (exams) high stakes activities show the highest engagement levels, followed by the engagement of students in active learning. In the teacher workshop we find that group work and active learning approaches increase engagement. However, not all active learning instruction result in measurable increases in engagement. In this presentation we discuss methodological challenges and limitations of the method and demonstrate a toolkit for data analysis. Our results have the potential to inform instructional design of learning environments around climate science.