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Inferring history-dependent memory strength
via collaborative filtering:
Toward the optimization of long-term retention
Robert Lindsey, Jeff Shroyer, and Michael C. Mozer
University of Colorado at Boulder
Effective teaching requires an understanding of a student's dynamic
knowledge state. To
facilitate
automated teaching, our goal is to construct models that infer the
strength of specific concepts, skills, or facts. Regardless of the
nature of the material, forgetting occurs, and the strength of memory
depends on the amount and temporal distribution of past study.
The challenge of inference is that available evidence
is quite weak. For example, suppose that a student solved four out of
five specific long-division problems correctly on a quiz; how well
would you expect the student to do on a particular long-division
problem assigned a month later? To overcome the sparsity of
observations, we use a collaborative filtering approach that
leverages
information about a population of students studying a population of
items to infer how well a specific student has
learned a specific item. We propose a hierarchical Bayesian
additive-factor model that considers the temporal distribution of past
study. We evaluate the efficacy of this model in predicting student recall
using data collected from vocabulary drill software we developed which is
being used by 180 students in a Denver-area middle school.
We further discuss how we are using predictive models to optimize
long-term retention of vocabulary.