Predicting and improving memory retention: Psychological theory matters in the big data era

Big Data in Cognitive Science

Volumn , Issue , 2016, Pages 34-64

  Mozer, Michael C ^a   Lindsey, Robert V ^a  

^a UNIVERSITY OF COLORADO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIR NAVIGATION; LEARNING SYSTEMS; POPULATION STATISTICS; STUDENTS;

COGNITIVE PSYCHOLOGY; FOREIGN LANGUAGE; MACHINE LEARNING TECHNIQUES; MEMORY RETENTION; PSYCHOLOGICAL THEORY; SPECIFIC MATERIALS; SUPPORT LEARNING; SYSTEMATIC REVIEW;

BIG DATA;

EID: 85019807118     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9781315413570     Document Type: Chapter

References (57)

1. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111, 1036-1060.
2. Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the LISP tutor. Cognitive Science, 13, 467-506.
3. Andrade, D. F., & Tavares, H. R. (2005). Item response theory for longitudinal data: Population parameter estimation. Journal of Multivariate Analysis, 95, 1-22.
4. Benjamin, A. S., & Tullis, J. (2010). What makes distributed practice effective? Cognitive Psychology, 61, 228-247.
5. Bjork, R. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185-205). Cambridge, MA: MIT Press.
6. Carpenter, S. K., Cepeda, N. J., Rohrer, D., Kang, S. H. K., & Pashler, H. (2012). Using spacing to enhance diverse forms of learning: Review of recent research and implications for instruction. Educational Psychology Review, 24, 369-378.
7. Carpenter, S. K., Pashler, H., & Cepeda, N. (2009). Using tests to enhance 8th grade students’ retention of U. S. history facts. Applied Cognitive Psychology, 23, 760-771.
8. Cen, H., Koedinger, K., & Junker, B. (2006). Learning factors analysis-a general method for cognitive model evaluation and improvement. In Proceedings of the Eighth International Conference on Intelligent Tutoring Systems.
9. Cen, H., Koedinger, K., & Junker, B. (2008). Comparing two IRT models for conjunctive skills. In Woolf, B., Aimeur, E., Njambou, R, & Lajoie, S. (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems.
10. Cepeda, N. J., Coburn, N., Rohrer, D., Wixted, J. T., Mozer, M. C., & Pashler, H. (2009). Optimizing distributed practice: Theoretical analysis and practical implications. Journal of Experimental Psychology, 56, 236-246.
11. Cepeda, N. J., Pashler, H., Vul, E., & Wixted, J. T. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin and Review, 132, 364-380.
12. Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132, 354-380.
13. Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects in learning: A temporal ridgeline of optimal retention. Psychological Science, 19, 1095-1102.
14. Cohen, M. S., Yan, V. X., Halamish, V., & Bjork, R. A. (2013). Do students think that difficult or valuable materials should be restudied sooner rather than later? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(6), 1682-1696.
15. Custers, E. (2010). Long-term retention of basic science knowledge: A review study. Advances in Health Science Education: Theory & Practice, 15(1), 109-128.
16. Custers, E., & ten Cate, O. (2011). Very long-term retention of basic science knowledge in doctors after graduation. Medical Education, 45(4), 422-430.
17. DeBoek, P., & Wilson, M. (Eds.). (2004). Explanatory item response models: A generalized linear and nonlinear approach. New York: Springer.
18. Dunlosky, J., Rawson, K., Marsh, E., Nathan, M., & Willingham, D. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58.
19. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.
20. Grimaldi, P. J., Pyc, M. A., & Rawson, K. A. (2010). Normative multitrial recall performance, metacognitive judgments and retrieval latencies for Lithuanian-English paired associates. Behavioral Research Methods, 42, 634-642.
21. Kang, S. H. K., Lindsey, R. V., Mozer, M. C., & Pashler, H. (2014). Retrieval practice over the long term: Expanding or equal-interval spacing? Should spacing be expanding or equal-interval? Psychonomic Bulletin & Review, 21, 1544-1550.
22. Khajah, M., Lindsey, R. V., & Mozer, M. C. (2013). Maximizing students’ retention via spaced review: Practical guidance from computational models of memory. In Proceedings of the 35th Annual Conference of the Cognitive Science Society.
23. Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61-78). Cambridge, UK: Cambridge University Press.
24. Kording, K. P., Tenenbaum, J. B., & Shadmehr, R. (2007). The dynamics of memory as a consequence of optimal adaptation to a changing body. Nature Neuroscience, 10, 779-786.
25. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. IEEE Computer, 42, 42-49.
26. Lan, A. S., Studer, C., & Baraniuk, R. G. (2014). Time-varying learning and content analytics via sparse factor analysis. In ACM SIGKDD conf. on knowledge disc. and data mining. Retrieved from http://arxiv.org/abs/1312.5734.
27. Leitner, S. (1972). So lernt man lernen. Angewandte Lernpsychologie - ein Weg zum Erfolg. Freiburg im Breisgau: Verlag Herder.
28. Lindsey, R. V. (2014). Probabilistic models of student learning and forgetting (Unpublished doctoral dissertation). Computer Science Department, University of Colorado at Boulder, USA.
29. Lindsey, R. V., Lewis, O., Pashler, H., & Mozer, M. C. (2010). Predicting students’ retention of facts from feedback during training. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2332-2337). Austin, TX: Cognitive Science Society.
30. Lindsey, R. V., Shroyer, J. D., Pashler, H., & Mozer, M. C. (2014). Improving students’ long-term knowledge retention through personalized review. Psychological Science, 25, 639-647.
31. Martin, J., & VanLehn, K. (1995). Student assessment using Bayesian nets. International Journal of Human-Computer Studies, 42, 575-591.
32. Masson, M., & Loftus, G. (2003). Using confidence intervals for graphically based data interpretation. Canadian Journal of Experimental Psychology, 57, 203-220.
33. Metcalfe, J., & Finn, B. (2011). People’s hypercorrection of high confidence errors: Did they know it all along? Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 437-448.
34. Mettler, E., & Kellman, P. J. (2014). Adaptive response-time-based category sequencing in perceptual learning. Vision Research, 99, 111-123.
35. Mettler, E., Massey, C., & Kellman, P. J. (2011). Improving adaptive learning technology through the use of response times. In L. Carlson, C. Holscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2532-2537). Austin, TX: Cognitive Science Society.
36. Mozer, M. C., Pashler, H., Cepeda, N., Lindsey, R. V., & Vul, E. (2009). Predicting the optimal spacing of study: A multiscale context model of memory. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems (Vol. 22, pp. 1321-1329). Boston, MA: MIT Press.
37. Mullany, A. (2013). A Q&A with Salman Khan. Retrieved December 23, 2014, from http://live.fastcompany.com/Event/A_QA_With_Salman_Khan.
38. Patz, R. J., & Junker, B. W. (1999). A straightforward approach to Markov chain Monte Carlo methods for item response models. Journal of Educational and Behavioral Statistics, 24, 146-178.
39. Pavlik, P. I. (2007). Understanding and applying the dynamics of test practice and study practice. Instructional Science, 35, 407-441.
40. Pavlik, P. I., & Anderson, J. R. (2005a). Practice and forgetting effects on vocabulary memory: An activation-based model of the spacing effect. Cognitive Science, 29(4), 559-586.
41. Pavlik, P. I., & Anderson, J. (2005b). Practice and forgetting effects on vocabulary memory: An activation-based model of the spacing effect. Cognitive Science, 29, 559-586.
42. Pavlik, P. I., & Anderson, J. R. (2008). Using a model to compute the optimal schedule of practice. Journal of Experimental Psychology: Applied, 14, 101-117.
43. Pavlik, P. I., Cen, H., & Koedinger, K. (2009). Performance factors analysis-a new alternative to knowledge tracing. In V. Dimitrova & R. Mizoguchi (Eds.), Proceeding of the Fourteenth International Conference on Artificial Intelligence in Education. Brighton, UK.
44. Raaijmakers, J. G.W. (2003). Spacing and repetition effects in human memory: Application of the SAM model. Cognitive Science, 27, 431-452.
45. Rickard, T., Lau, J., & Pashler, H. (2008). Spacing and the transition from calculation to retrieval. Psychonomic Bulletin & Review, 15, 656-661.
46. Ridgeway, K., Mozer, M. C., & Bowles, A. (2016). Forgetting of foreign-language skills: A corpus-based analysis of online tutoring software. Cognitive Science Journal. (Accepted for publication).
47. Rohrer, D., & Taylor, K. (2006). The effects of overlearning and distributed practice on the retention of mathematics knowledge. Applied Cognitive Psychology, 20, 1209-1224.
48. Roussos, L. A., Templin, J. L., & Henson, R. A. (2007). Skills diagnosis using IRT-based latent class models. Journal of Educational Measurement, 44, 293-311.
49. Seabrook, R., Brown, G., & Solity, J. (2005). Distributed and massed practice: From laboratory to classroom. Applied Cognitive Psychology, 19, 107-122.
50. Sobel, H., Cepeda, N., & Kapler, I. (2011). Spacing effects in real-world classroom vocabulary learning. Applied Cognitive Psychology, 25, 763-767.
51. Sohl-Dickstein, J. (2013). Personalized learning and temporal modeling at Khan Academy. Retrieved from http://lytics.stanford.edu/datadriveneducation/slides/sohldickstein. pdf.
52. Staddon, J. E. R., Chelaru, I. M., & Higa, J. J. (2002). Habituation, memory and the brain: The dynamics of interval timing. Behavioural Processes, 57, 71-88.
53. van Lehn, K., Jordan, P., & Litman, D. (2007). Developing pedagogically effective tutorial dialogue tactics: Experiments and a testbed. In Proceedings of the SLaTE Workshop on Speech and Language (pp. 17-20).
54. Wagenmakers, E.-J., Grünwald, P., & Steyvers, M. (2006). Accumulative prediction error and the selection of time series models. Journal of Mathematical Psychology, 50, 149-166.
55. Wixted, J. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55, 235-269.
56. Wixted, J. T., & Carpenter, S. K. (2007). The Wickelgren power law and the Ebbinghaus savings function. Psychological Science, 18, 133-134.
57. Woźniak, P. (1990). Optimization of learning (Unpublished master’s thesis). Poznan University of Technology, Poznan, Poland.