Pedagogical Activity Selection:
Drawing Insight From Sequential
Decision Making Under Uncertainty 

Emma Brunskill
Computer Science Department
Carnegie Mellon University

What activity should be given to a student at each interaction to best help the student learn? This question represents one of the key challenges facing any human or automated teacher, and good answers to it could have a profound impact on education. Sequential decision making under uncertainty offers us a powerful and flexible frameworks for modeling adaptive pedagogical activity selection. I will discuss two of my projects that pose tutor activity selection as sequential decision making under uncertainty:  an algorithm for scaling up to the large domains common in education, and an analysis of the impact of individual variance in decision process parameters on the resulting best policies. I'll also briefly mention open issues.