University of Pittsburgh

Predicting and boosting memory retention

Department of Computer Science ‚Äčand Institute of Cognitive Science‚Äč
Friday, March 31, 2017 - 12:30pm - 1:30pm

Cognitive psychology has long aimed to understand mechanisms of human memory, with the hope that such an understanding will yield practical techniques that support long-term retention of newly learned material.  Although research insights have given rise to qualitative advice for students and educators, we present a complementary approach that offers quantitative, individualized guidance.  Our approach synthesizes theory-driven and data-driven methodologies.  Psychological theory characterizes basic mechanisms of human memory shared among members of a population, whereas data mining techniques use observations from a population to make inferences about individuals.  We argue that despite the power of big data, psychological theory provides essential constraints on models. We present models of forgetting and spaced practice that predict the dynamic time-varying knowledge state of an individual student for specific material.  We incorporate these models into retrieval-practice software to assist students in reviewing previously mastered material. In a year-long intervention in middle-school foreign language courses, we demonstrate the value of systematic review on long-term educational outcomes, but more specifically, the value of adaptive review that leverages data from a population of learners to personalize recommendations based on an individual's study history and past performance.  If I talk fast, I'd also like to describe ongoing work that leverages theories of human memory to improve recurrent neural nets that predict event sequences.    

  This work is a joint collaboration with Robert Lindsey and Karl Ridgeway at the University of Colorado.


Michael Mozer received a Ph.D. in Cognitive Science at the University of California at San Diego in 1987.  Following a postdoctoral fellowship with Geoffrey Hinton at the University of Toronto, he joined the faculty at the University of Colorado at Boulder and is presently an Professor in the Department of Computer Science and the Institute of Cognitive Science.  He is secretary of the Neural Information Processing Systems Foundation and has served as chair of the Cognitive Science Society. He is interested both in developing machine learning algorithms that leverage insights from human cognition, and in developing software tools to optimize human performance using machine learning methods.

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