Probabilistic Models of Student Learning and Forgetting

Graduate Thesis Or Dissertation

Citations

Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/zp38wc97m

Abstract

This thesis uses statistical machine learning techniques to construct predictive models of human learning and to improve human learning by discovering optimal teaching methodologies. In Chapters 2 and 3, I present and evaluate models for predicting the changing memory strength of material being studied over time. The models combine a psychological theory of memory with Bayesian methods for inferring individual differences. In Chapter 4, I develop methods for delivering efficient, systematic, personalized review using the statistical models. Results are presented from three large semester-long experiments with middle school students which demonstrate how this "big data" approach to education yields substantial gains in the long-term retention of course material. In Chapter 5, I focus on optimizing various aspects of instruction for populations of students. This involves a novel experimental paradigm which combines Bayesian nonparametric modeling techniques and probabilistic generative models of student performance. In Chapters 6 and 7, I present supporting laboratory behavioral studies and theoretical analyses. These include an examination of the relationship between study format and the testing effect, and a parsimonious theoretical account of long-term recency effects.

Creator