Date of Award

Summer 7-15-2014

Document Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Michael Mozer

Second Advisor

Aaron Clauset

Third Advisor

Vanja Dukic

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.

Share

COinS