Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era

Abstract

Human memory is fragile. The initial acquisition of knowledge is slow andeffortful. And once mastery is achieved, the knowledge must be exercisedperiodically to mitigate forgetting. Understanding the cognitive mechanisms ofmemory has been a longstanding goal of modern experimental psychology, withthe hope that such an understanding will lead to practical techniques that supportlearning and retention. Our specific aim is to go beyond the traditional qualitativeforms of guidance provided by psychology and express our understanding in termsof computational models that characterize the temporal dynamics of a learner’sknowledge state. This knowledge state specifies what material the individual alreadygrasps well, what material can be easily learned, and what material is on the vergeof slipping away. Given a knowledge-state model, individualized teaching strategiescan be constructed that select material to maximize instructional effectiveness.

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