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

Cognitive psychology has long had the aim of understanding mechanisms of human memory, with the expectation that such an understanding will yield practical techniques that support learning and retention. Although research insights have given rise to qualitative advice for students and educators, we present a complementary approach that offers quantitative, individualized guidance. Our approach synthesizes theory-driven and data-driven methodologies. Psychological theory characterizes basic mechanisms of human memory shared among members of a population, whereas machine-learning techniques use observations from a population to make inferences about individuals. We argue that despite the power of big data, psychological theory provides essential constraints on models. We present models of forgetting and spaced practice that predict the dynamic time-varying knowledge state of an individual student for specific material. We incorporate these models into retrieval-practice software to assist students in reviewing previously mastered material. In an ambitious year-long intervention in a middle-school foreign language course, we demonstrate the value of systematic review on long-term educational outcomes, but more specifically, the value of adaptive review that leverages data from a population of learners to personalize recommendations based on an individual’s study history and past performance.

[1]  Ed Vul,et al.  Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory , 2009, NIPS.

[2]  Veronica X. Yan,et al.  Do students think that difficult or valuable materials should be restudied sooner rather than later? , 2013, Journal of experimental psychology. Learning, memory, and cognition.

[3]  Deniz Senturk-Doganaksoy,et al.  Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach , 2006, Technometrics.

[4]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[5]  M. Masson.  Using confidence intervals for graphically based data interpretation. , 2003, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[6]  Philip J. Kellman,et al.  Adaptive response-time-based category sequencing in perceptual learning , 2014, Vision Research.

[7]  E. Custers,et al.  Very long‐term retention of basic science knowledge in doctors after graduation , 2011, Medical education.

[8]  Philip J. Kellman,et al.  Improving Adaptive Learning Technology through the Use of Response Times , 2011, CogSci.

[9]  Michael C. Mozer,et al.  Maximizing Students' Retention Via Spaced Review: Practical Guidance From Computational Models Of Memory , 2013, CogSci.

[10]  E. Custers.  Long-term retention of basic science knowledge: a review study , 2010, Advances in health sciences education : theory and practice.

[11]  Konrad Paul Kording,et al.  The dynamics of memory as a consequence of optimal adaptation to a changing body , 2007, Nature Neuroscience.

[12]  Edward Vul,et al.  PSYCHOLOGICAL SCIENCE Research Article Spacing Effects in Learning A Temporal Ridgeline of Optimal Retention , 2022 .

[13]  John R Anderson,et al.  An integrated theory of the mind. , 2004, Psychological review.

[14]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[15]  Phillip J. Grimaldi,et al.  Normative multitrial recall performance, metacognitive judgments, and retrieval latencies for Lithuanian—English paired associates , 2010, Behavior research methods.

[16]  Kenneth R. Koedinger,et al.  Performance Factors Analysis - A New Alternative to Knowledge Tracing , 2009, AIED.

[17]  Philip I. Pavlik,et al.  Understanding and applying the dynamics of test practice and study practice , 2007 .

[18]  Peter Grünwald,et al.  Accumulative prediction error and the selection of time series models , 2006 .

[19]  J. Metcalfe,et al.  People's hypercorrection of high-confidence errors: did they know it all along? , 2011, Journal of experimental psychology. Learning, memory, and cognition.

[20]  Robert V. Lindsey,et al.  Improving Students’ Long-Term Knowledge Retention Through Personalized Review , 2014, Psychological science.

[21]  D. Andrade,et al.  Item response theory for longitudinal data: population parameter estimation , 2005 .

[22]  Harold Pashler,et al.  Using tests to enhance 8th grade students' retention of U.S. history facts , 2009 .

[23]  P. Boeck,et al.  Explanatory item response models : a generalized linear and nonlinear approach , 2004 .

[24]  Jeroen G. W. Raaijmakers,et al.  Spacing and repetition effects in human memory: application of the SAM model , 2003, Cogn. Sci..

[25]  Michael C. Mozer,et al.  Forgetting of Foreign-Language Skills: A Corpus-Based Analysis of Online Tutoring Software , 2017, Cogn. Sci..

[26]  John R. Anderson,et al.  Practice and Forgetting Effects on Vocabulary Memory: An Activation-Based Model of the Spacing Effect , 2005, Cogn. Sci..

[27]  Kenneth R. Koedinger,et al.  Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement , 2006, Intelligent Tutoring Systems.

[28]  Predicting Students ’ Retention of Facts from Feedback During Training , 2010 .

[29]  Harold Pashler,et al.  Optimizing distributed practice: theoretical analysis and practical implications. , 2009, Experimental psychology.

[30]  Shana K. Carpenter,et al.  Using Spacing to Enhance Diverse Forms of Learning: Review of Recent Research and Implications for Instruction , 2012 .

[31]  J. Staddon,et al.  Habituation, memory and the brain: the dynamics of interval timing , 2002, Behavioural Processes.

[32]  John R. Anderson,et al.  Skill Acquisition and the LISP Tutor , 1989, Cogn. Sci..

[33]  Harold Pashler,et al.  Retrieval practice over the long term: Should spacing be expanding or equal-interval? , 2014, Psychonomic bulletin & review.

[34]  Richard G. Baraniuk,et al.  Time-varying learning and content analytics via sparse factor analysis , 2013, KDD.

[35]  John R Anderson,et al.  Using a model to compute the optimal schedule of practice. , 2008, Journal of experimental psychology. Applied.

[36]  Kurt VanLehn,et al.  Developing pedagogically effective tutorial dialogue tactics: experiments and a testbed , 2007, SLaTE.

[37]  H. Pashler,et al.  Distributed practice in verbal recall tasks: A review and quantitative synthesis. , 2006, Psychological bulletin.

[38]  Jonathan Solity,et al.  (www.interscience.wiley.com) DOI: 10.1002/acp.1066 Distributed and Massed Practice: From Laboratory to Classroom , 2022 .

[39]  Joel D. Martin,et al.  Student assessment using Bayesian nets , 1995, Int. J. Hum. Comput. Stud..

[40]  Peter Maurer,et al.  The Cambridge Handbook of the Learning Sciences , 2022 .

[41]  Robert V. Lindsey.  Probabilistic Models of Student Learning and Forgetting , 2014 .

[42]  R. Bjork.  Memory and metamemory considerations in the training of human beings. , 1994 .

[43]  Kelli M Taylor,et al.  The effects of overlearning and distributed practise on the retention of mathematics knowledge , 2006 .

[44]  Aaron S. Benjamin,et al.  What makes distributed practice effective? , 2010, Cognitive Psychology.

[45]  Nicholas J. Cepeda,et al.  Spacing Effects in Real-World Classroom Vocabulary Learning , 2011 .

[46]  Shana K. Carpenter,et al.  The Wickelgren Power Law and the Ebbinghaus Savings Function , 2007, Psychological science.

[47]  Timothy C. Rickard,et al.  Spacing and the transition from calculation to retrieval , 2008, Psychonomic bulletin & review.

[48]  John Dunlosky,et al.  Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology , 2012 .

[49]  J. Templin,et al.  Skills Diagnosis Using IRT-Based Latent Class Models , 2007 .

[50]  Kenneth R. Koedinger,et al.  Comparing Two IRT Models for Conjunctive Skills , 2008, Intelligent Tutoring Systems.

[51]  Richard J. Patz,et al.  A Straightforward Approach to Markov Chain Monte Carlo Methods for Item Response Models , 1999 .

[52]  J. Wixted.  The psychology and neuroscience of forgetting. , 2004, Annual review of psychology.