nips nips2013 nips2013-241 nips2013-241-reference knowledge-graph by maker-knowledge-mining
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Author: Robert Lindsey, Michael Mozer, William J. Huggins, Harold Pashler
Abstract: Psychologists are interested in developing instructional policies that boost student learning. An instructional policy specifies the manner and content of instruction. For example, in the domain of concept learning, a policy might specify the nature of exemplars chosen over a training sequence. Traditional psychological studies compare several hand-selected policies, e.g., contrasting a policy that selects only difficult-to-classify exemplars with a policy that gradually progresses over the training sequence from easy exemplars to more difficult (known as fading). We propose an alternative to the traditional methodology in which we define a parameterized space of policies and search this space to identify the optimal policy. For example, in concept learning, policies might be described by a fading function that specifies exemplar difficulty over time. We propose an experimental technique for searching policy spaces using Gaussian process surrogate-based optimization and a generative model of student performance. Instead of evaluating a few experimental conditions each with many human subjects, as the traditional methodology does, our technique evaluates many experimental conditions each with a few subjects. Even though individual subjects provide only a noisy estimate of the population mean, the optimization method allows us to determine the shape of the policy space and to identify the global optimum, and is as efficient in its subject budget as a traditional A-B comparison. We evaluate the method via two behavioral studies, and suggest that the method has broad applicability to optimization problems involving humans outside the educational arena. 1
Anderson, E. J., & Ferris, M. C. (2001). A direct search algorithm for optimization with noisy function evaluations. SIAM Journal of Optimization, 11 , 837–857. 8 Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the LISP tutor. Cognitive Science, 13 , 467–506. Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009, June). Curriculum learning. In L. Bottou & M. Littman (Eds.), Proceedings of the 26th international conference on machine learning (pp. 41–48). Montreal: Omnipress. Boeck, P. D., & Wilson, M. (2004). Explanatory item response models. a generalized linear and nonlinear approach. New York: Springer. Carvalho, P. F., & Goldstone, R. L. (2011, November). Stimulus similarity relations modulate benefits for blocking versus interleaving during category learning. (Presentation at the 52nd Annual Meeting of the Psychonomics Society, Seattle, WA) Chi, M., VanLehn, K., Litman, D., & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Modeling and User-Adapted Interaction. Special Issue on Data Mining for Personalized Educational Systems, 21 , 137–180. Christian, B. (2012). The A/B test: Inside the technology that’s changing the rules of business. Wired , 20(4). de Jonge, M., Tabbers, H. K., Pecher, D., & Zeelenberg, R. (2012). The effect of study time distribution on learning and retention: A goldilocks principle for presentation rate. J. Exp. Psych.: Learning, Mem., & Cog., 38 , 405–412. Forrester, A. I. J., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences, 45 , 50–79. Goldstone, R. L., & Steyvers, M. (2001). The sensitization and differentiation of dimensions during category learning. Journal of Experimental Psychology: General , 130 , 116–139. Kang, S. H. K., & Pashler, H. (2011). Learning painting styles: Spacing is advantageous when it promotes discriminative contrast. Applied Cognitive Psychology, 26 , 97–103. Khan, F., Zhu, X. J., & Mutlu, B. (2011). How do humans teach: On curriculum learning and teaching dimension. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, & K. Weinberger (Eds.), Adv. in NIPS 24 (pp. 1449–1457). La Jolla, CA: NIPS Found. 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. Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories: Is spacing the enemy of induction? Psychological Science, 19 , 585–592. Martin, J., & VanLehn, K. (1995). Student assessment using bayesian nets. International Journal of Human-Computer Studies, 42 , 575–591. Minear, M., & Park, D. C. (2004). A lifespan database of adult facial stimuli. Behavior Research Methods, Instruments, and Computers, 36 , 630–633. Murray, I., Adams, R. P., & MacKay, D. J. (2010). Elliptical slice sampling. J. of Machine Learn. Res., 9 , 541–548. Osborne, M. A., Garnett, R., & Roberts, S. J. (2009, January). Gaussian processes for global optimization. In 3d intl. conf. on learning and intell. opt. Trento, Italy. Pashler, H., & Mozer, M. C. (in press). Enhancing perceptual category learning through fading: When does it help? J. of Exptl. Psych.: Learning, Mem., & Cog.. Rafferty, A. N., Brunskill, E. B., Griffiths, T. L., & Shafto, P. (2011). Faster teaching by POMDP planning. In Proc. of the 15th intl. conf. on AI in education. Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, 4 , 409–435. Salmon, J. P., McMullen, P. A., & Filliter, J. H. (2010). Norms for two types of manipulability (graspability and functional usage), familiarity, and age of acquisition for 320 photographs of objects. Behavioral Research Methods, 42 , 82–95. Schloss, K. B., & Palmer, S. E. (2011). Aesthetic response to color combinations: preference, harmony, and similarity. Attention, Perception, & Psychophysics, 73 , 551–571. Srinivas, N., Krause, A., Kakade, S., & Seeger, M. (2010). Gaussian process optimization in the bandit setting: No regret and experimental design. In Proceedings of the 27th international conference on machine learning. Haifa, Israel. Whitehill, J., & Movellan, J. R. (2010). Optimal teaching machines (Tech. Rep.). La Jolla, CA: Department of Computer Science, UCSD. 9