ABSTRACT
Human memory is fragile. The initial acquisition of knowledge is slow and
effortful. And once mastery is achieved, the knowledge must be exercised
periodically to mitigate forgetting. Understanding the cognitive mechanisms of
memory has been a longstanding goal of modern experimental psychology, with
the hope that such an understanding will lead to practical techniques that support
learning and retention. Our specific aim is to go beyond the traditional qualitative
forms of guidance provided by psychology and express our understanding in terms
of computational models that characterize the temporal dynamics of a learner’s
knowledge state. This knowledge state specifies what material the individual already
grasps well, what material can be easily learned, and what material is on the verge
of slipping away. Given a knowledge-state model, individualized teaching strategies
can be constructed that select material to maximize instructional effectiveness.
