Machine learning algorithms have been widely used for educational applications, such as predicting who might be at risk of failing a course, or sometimes referred to as identifying at-risk students. Existing research has focused on developing algorithms to improve the timeliness and accuracy of such predictions. Such a focus implies limited learning activity data and an overreliance on demographic background variables, which brings forth fairness concerns. In this talk, Dr. Cheng introduced two studies examining ed the utility of predictive modeling in early identification of at-risk students in two foundational STEM courses, respectively: organic chemistry (college level) and statistics (high school level). Then I will she showed how they evaluated the performance of the predictive models using various fairness metrics and explored the efficacy of different bias mitigation strategies in the second study. Finally, she discussed the implications of the findings in terms of fair and explainable AI.
Dr. Ying (“Alison”) Cheng is Professor of Quantitative Psychology and Fellow of the Institute for Educational Initiatives at University of Notre Dame and also the Associate Director of Education at the Lucy Family Institute of Data and Society at Notre Dame.
Info: bit.ly/ai4-yin...
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Негізгі бет Craft Data Science Insights - Session 4: Dr. Ying Cheng
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