Perspectives on Computational Models of Learning and Forgetting

Technological developments have spawned a range of educational software that strives to enhance learning through personalized adaptation. The success of these systems depends on how accurate the knowledge state of individual learners is modeled over time. Computer scientists have been at the forefront of development for these kinds of distributed learning systems and have primarily relied on data-driven algorithms to trace knowledge acquisition in noisy and complex learning domains. Meanwhile, research psychologists have primarily relied on data collected in controlled laboratory settings to develop and validate theory-driven computational models, but have not devoted much exploration to learning in naturalistic environments. The two fields have largely operated in parallel despite considerable overlap in goals. We argue that mutual benefits would result from identifying and implementing more accurate methods to model the temporal dynamics of learning and forgetting for individual learners. Here we discuss recent efforts in developing adaptive learning technologies to highlight the strengths and weaknesses inherent in the typical approaches of both fields. We argue that a closer collaboration between the educational machine learning/data mining and cognitive psychology communities would be a productive and exciting direction for adaptive learning system application to move in.