Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R. (2009). A new paradigm for intelligent tutoring systems: example-tracing tutors. International Journal of Artificial Intelligence in Education
, 105–154.Google Scholar
Anderson, J.R. (1993). Rules of the mind
. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
Carnahan, H., Van Eerd, D.L., Allard, F. (1990). A note on the relationship between task requirements and the contextual interference effect. Journal of Motor Behavior
(1), 159–169.CrossRefGoogle Scholar
Chan, T.-W., & Chou, C.-Y. (1997). Exploring the design of computer supports for reciprocal tutoring. International Journal of Artificial Intelligence in Education
, 1–29.Google Scholar
Chase, W.G., & Simon, H.A. (1973). Perception in chess. Cognitive Psychology
(1), 55–81.CrossRefGoogle Scholar
Del Rey, P. (1982). Effects of contextual interference on the memory of older females differing in levels of physical activity. Perceptual and Motor Skills
(1), 171–180.CrossRefGoogle Scholar
Dietterich, T.G. (1986). Learning at the knowledge level. Machine Learning
(3), 287–315.Google Scholar
Forgy, C. (1982). Rete: a fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligences
(1), 17–37.CrossRefGoogle Scholar
French, K.E., Rink, J.E., Werner, P.F. (1990). Effects of contextual interference on retention of three volleyball skills. Peceptual and Motor Skills
, 179–186.Google Scholar
Gabriele, T.E., Hall, C.R., Buckolz, E.E. (1987). Practice schedule effects on the acquisition and retention of a motor skill. Human Movement Science
, 1–16.CrossRefGoogle Scholar
Jelsma, O., & Pieters, J.M. (1989). Practice schedule and cognitive style interaction in learning a maze task. Applied Cognitive Psychology
(1), 73–83.CrossRefGoogle Scholar
Koedinger, K.R., & Anderson, J.R. (1990). Abstract planning and perceptual chunks: elements of expertise in geometry. Cognitive Science
, 511–550.CrossRefGoogle Scholar
Koedinger, K.R., Baker, R.SJ., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J. (2010). A data repository for the EDM community: the PSLC DataShop. Handbook of educational data mining (pp. 43–55).
Laird, J.E., Newell, A., Rosenbloom, P.S. (1987). Soar: an architecture for general intelligence. Artificial Intelligence
(1), 1–64.MathSciNetCrossRefGoogle Scholar
Langley, P., & Choi, D. (2006). A unified cognitive architecture for physical agents. In Proceedings of the twenty-first national conference on artificial intelligence (pp. 1469–1474).
Lau, T., & Weld, D.S. (1998). Programming by demonstration: An inductive learning formulation. In Proceedings of the 1999 international conference on intelligence user interfaces (pp. 145–152).
Lee, T.D., & Magill, R.A. (1983). The locus of contextual interference in motor-skill acquisition. Journal Of Experimental Psychology, Learning Memory And Cognition
(4), 730–746.CrossRefGoogle Scholar
Li, N., Cohen, W.W., Koedinger, K.R. (2010). A computational model of accelerated future learning through feature recognition. In Proceedings of 10th international conference on intelligent tutoring systems (pp. 368–370).
Li, N., Cohen, W.W., Matsuda, N., Koedinger, K.R. (2011). A machine learning approach for automatic student model discovery. In Proceedings of the 4th international conference on educational data mining (pp. 31–40).
Li, N., Cohen, W.W., Koedinger, K.R. (2012a). Efficient cross-domain learning of complex skills. In Proceedings of the 11th international conference on intelligent tutoring systems (pp. 493–498).
Li, N., Stracuzzi, D., Langley, P. (2012b). Improving acquisition of teleoreactive logic programs through representation change. Advances in Cognitive Systems
, 109–126.Google Scholar
Li, N., Cohen, W.W., Koedinger, K.R. (2012c). Integrating representation learning and skill learning in a human-like intelligent agent. Technical Report CMU-MLD-12-1001, Carnegie Mellon University.
Li, N., Tian, Y., Cohen, W.W., Koedinger, K.R. (2013). Integrating perceptual learning with external world knowledge in a simulated student. In 16th international conference on artificial intelligence in education (pp. 400–410).
Matsuda, N., Cohen, W.W., Sewall, J., Lacerda, G., Koedinger, K.R. (2008). Why tutored problem solving may be better than example study: Theoretical implications from a simulated-student study. In Proceedings of the 9th international conference on intelligent tutoring system (pp. 111–121).
McLaren, B.M., Lim, S.-j., Koedinger, K.R. (2008). When and how often should worked examples be given to students? New results and a summary of the current state of research why isn’t the science done? Cognitive Science, 2176–2181.
Mitchell, T. (1982). Generalization as search. Artificial Intelligence
(2), 203–226.MathSciNetCrossRefGoogle Scholar
Monsell, S. (2003). Task switching. Trends in Cognitive Sciences
(3), 134–140.CrossRefGoogle Scholar
Muggleton, S., & de Raedt, L. (1994). Inductive logic programming: theory and methods. Journal of Logic Programming
, 629–679.MathSciNetCrossRefGoogle Scholar
Ohlsson, S. (2008). Computational models of skill acquisition, chapter 13 (pp. 359–395). Cambridge University Press.
Pentti Hietala, T.N. (1998). The competence of learning companion agents. International Journal of Artificial Intelligence in Education
, 178–192.Google Scholar
Quinlan, J.R. (1990). Learning logical definitions from relations. Machine Learning
(3), 239–266.Google Scholar
Richman, H.B., Staszewski, J.J., Simon, H.A. (1995). Simulation of expert memory using EPAM IV. Psychological Review
(2), 305–330.CrossRefGoogle Scholar
Sekiya, H., Magill, R.A., Anderson, D.I. (1996). The contextual interference effect in parameter modifications of the same generalized motor program. Research Quarterly for Exercise and Sport
(1), 59–68.CrossRefGoogle Scholar
Shea, JB., & Morgan, R.L. (1979). Contextual interference effects on the acquisition, retention, and transfer of a motor skill. Journal of Experimental Psychology Human Learning Memory
(2), 179–187.CrossRefGoogle Scholar
Stampfer, E., Long, Y., Aleven, V., Koedinger, K.R. (2011). Eliciting intelligent novice behaviors with grounded feedback in a fraction addition tutor. In Proceedings of the 15th international conference on artificial intelligence in education, AIED’11 (pp. 560–562). Springer-Verlag.
Tsutsui, S., Lee, T.D., Hodges, N.J. (1998). Contextual interference in learning new patterns of bimanual coordination. Journal of Motor Behavior
(2), 151–157.CrossRefGoogle Scholar
VanLehn, K. (1987). Learning one subprocedure per lesson. Artificial Intelligence
, 1–40.CrossRefGoogle Scholar
VanLehn, K. (1990). Mind bugs: The origins of procedural misconceptions
. Cambridge: MIT Press.Google Scholar
Wulf, G., & Shea, C.H. (2002). Principles derived from the study of simple skills do not generalize to complex skill learning. Psychonomic Bulletin Review
(2), 185–211.CrossRefGoogle Scholar
Young, D.E., Cohen, M.J., Husak, W.S. (1993). Contextual interference and motor skill acquisition: on the processes that influence retention. Human Movement Science
(5), 577–600.CrossRefGoogle Scholar