BIGDATA: EAGER: Catalyzing Research in Multimodal Learning Analytics

Northwestern University, Evanston IL

Investigators

Abstract

Data science techniques have revolutionized many academic fields and led to terrific gains in the commercial sector. They have to date been underutilized in solving critical problems in the US educational system, particularly in understanding Science, Technology, Engineering and Mathematics (STEM) learning and learning environments, broadening participation in STEM, and increasing retention for students traditionally underserved in STEM. The goals of the Directorate for Education and Human Resources through the Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA) program are to advance fundamental research aimed at understanding and solving these critical problems, and to catalyze the use of data science in Education Research. As more open-ended learning environments are being employed in schools, it is critical to understand how they affect learning, motivation and engagement in STEM. The most widely used methods in educational research are inadequate for addressing these questions because they do not address the scale and complexity of the data provided from these environments. The current standard practice in learning science and education research for analyzing these types of data is to record student activity in these spaces on audio or video, create qualitative coding schemes, and code the data by hand. This is labor intensive and prone to personal judgment and error. It also tends to result in research that is not replicable or scalable. For example, if each researcher studying important educational areas like collaboration or developing planning skills uses his or her own coding framework in their work, it makes it difficult to group the results of these studies and understand the bigger picture. In addition, it is often impossible for one research group to reproduce the results another research group found by trying to use the other group's coding framework. This Early Concept Grant for Exploratory Research (EAGER) will advance the understanding of how promising new technology environments affect these outcomes by bringing together experts in computer science, data science and education research to develop new methods of studying these environments that are not likely to have the same problems. By using data from new methods of data capture such as logs of interaction with the objects in a maker space or new data mining techniques to analyze data from audio or video recordings, the Principal Investigators seek to greatly increase the field's ability to learn about what people are learning in these environments. The Principal Investigators propose to bring together experts in data analysis, computer science, and educational research to develop new ways to pursue multimodal learning analytics (MLA). MLA opens the door to exploring learning environments, whether classrooms or design studios, that have been difficult to investigate before. New types of sensors and data mining techniques make it possible to capture the process data that is usually lost in the activities in these spaces. The PIs propose to achieve this through a series of workshops. The first will define directions and map potential tools to important constructs they can be used to measure. The middle workshops constitute a series of iterative cycles of tool development and refinement. The final hack-a-thon workshop will involve participants coding prototypes and full models of tools that will be openly available to the community.