A large amount of learning happens in digital environments that can preserve data about every click, scroll, and touch. These data can support powerful predictions about a learner’s interests, abilities, and actions. What can we do with these data, and what principles should guide our use of data? In this episode, Avriel Epps-Darling, Andrew Ho, and Katina Michael discuss the promise and peril of “big data” in educational contexts. Each will provide examples where these data have helped learners and improved equity, as well as situations where they have caused harm, even with the best of intentions.
These ideas have implications for data use in Learning Management Systems, Massive Open Online Courses, and Early Warning Indicator Systems, in contexts including K-12, higher education, and online learning. Panelists will also discuss principles for ethical and equitable use of data in education, including beneficence, transparency, informed consent, privacy, bias detection and bias prevention.
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