Lowering Barriers for Accessing Sensor Data in Education: Lessons Learned from Teaching Multimodal Learning Analytics to Educators

Abstract

In an increasingly data-driven world, large volumes of fine-grained data are infiltrating all aspects of our lives. The world of education is no exception to this phenomenon: in classrooms, we are witnessing an increasing amount of information being collected on learners and teachers. Because educational practitioners have so much contextual and practical knowledge about classroom management, we argue that data-mining workflows should be co-designed with them. This paper describes a class on Multimodal Learning Analytics taught to graduate students in education who used to be (or are planning to become) educators, teachers, school administrators and have little to no technical background. The course was designed to provide novices with career-relevant hands-on activities and facilitate personal engagement with data collection and analysis. We provide examples of student-created data mining workflows and the trajectory they followed to get a foundational understanding of data mining. Finally, we present survey data illustrating the strengths and weaknesses of the assignments and projects used in the class. We conclude with lessons learned and recommendations for implementing such a course at other institutions.