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Alyssa Wise

Credentials: Associate Professor of Learning Sciences and Educational Technology at New York University and the Director of LEARN, NYU's Learning Analytics Research Network

Alyssa Wise headshot

Dr. Alyssa Wise is Associate Professor of Learning Sciences and Educational Technology at New York University and the Director of LEARN, NYU’s pioneering university-wide Learning Analytics Research Network. She holds a PhD in Learning Sciences and an MS in Instructional Systems Technology from Indiana University as well as a B.S. in Chemistry from Yale University. Dr. Wise’s research is situated at the intersection of learning and educational data sciences, focusing on the design of innovative analytics systems that are theoretically grounded, computationally robust, and pedagogically useful for informing teaching and learning. Drawing on almost twenty years of experience designing educational tools and developing methods and metrics to evaluate learning, she examines how we can best use the data generated through online activity to enhance students’ learning experiences. Dr.Wise serves as Co-Editor-in-Chief of the Journal of Learning Analytics, is a Co-Editor of the Handbook of Learning Analytics, and has produced numerous high-impact publications on the ethical identification and application of useful traces of learning to inform educational decision-making.


The Data Dilemma

Online education continues to grow as a distinct form of teaching and learning, with the recent pandemic further intensifying this shift in lasting ways. Such expansion (and the collective anxiety of educators new to online learning environments) has brought intense attention to the many streams of data generated through online learning’s underlying technologies. For some, there is great allure in obtaining previously unavailable insight into what students are doing in order to better understand and support learning. For others, there is great concern in ceding aspects of pedagogical authority to algorithms we don’t fully understand built on data that is decidedly not neutral. In this talk I’ll open up the black box of educational data mining and learning analytics to show the kinds of insights we can (and can’t) currently glean from easily available data as well as what is possible when more sophisticated data collection and analysis procedures are used. Equally importantly, we’ll explore both how instructors, students, advisors and administrators can practically draw on analytic information in context to make better decisions, as well as the challenges to privacy, agency, equity and fairness such use may present. Drawing on a rich set of examples from our work at the Learning Analytics Research Network, we’ll consider how best to work towards a desirable future of learning analytics.