Duan, Xiaojing, Ambrose, G. Alex (2021) “Inclusive Learning Analytics Framework for Student Success in an Introductory STEM Course” Indiana University’s 3rd Annual Learning Analytics Summit: Data-informed Stories, Transformational Journeys.
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We present an inclusive learning analytics framework for identifying at-risk or rather “non-thriving” students in a large-enrollment introductory general chemistry course. With the overall goals of closing opportunity gaps, maximizing all students’ potential for course success, and increasing STEM retention rates, our study used a hybrid approach of combining predictive modeling and domain experts decision-making to identify underperforming students during the early part of the course. We recognize that different institutions will have different definitions of thriving and course structures, but the methods we used in our study provide scholar-practitioners with a set of tools that can be replicated and customized for STEM courses on their campus.