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Reports and Reflections: Three Decades of JLS

What happened to the interdisciplinary study of learning in humans and machines?

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Pages 663-681 | Received 10 Dec 2021, Accepted 14 Jun 2023, Published online: 12 Oct 2023

References

  • Abrahamson, D., & Sánchez-García, R. (2016). Learning is moving in new ways: The ecological dynamics of mathematics education. Journal of the Learning Sciences, 25(2), 203–239. https://doi.org/10.1080/10508406.2016.1143370
  • Abrahamson, D., & Wilensky, U. (2005). Piaget? Vygotsky? I’m game!–agent-based modeling for psychology research [Paper presentation]. The 35th Annual Meeting of the Jean Piaget Society. https://ccl.northwestern.edu/papers/Abrahamson_Wilensky_JPS20.pdf
  • Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167–207. https://doi.org/10.1207/s15327809jls0402_2
  • Baker, M. J. (2000). The roles of models in artificial intelligence and education research: A prospective view. International Journal of Artificial Intelligence and Education, 11(2), 122–143.
  • Barab, S. A., Cherkes-Julkowski, M., Swenson, R., Garrett, S., Shaw, R. E., & Young, M. (1999). Principles of self-organization: Learning as participation in autocatakinetic systems. Journal of the Learning Sciences, 8(3–4), 349–390. https://doi.org/10.1080/10508406.1999.9672074
  • Booker, A. N., Vossoughi, S., & Hooper, P. K. (2014). Tensions and possibilities for political work in the learning sciences. In J. L. Polman, E. A. Kyza, D. K. O'Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O'Connor, T. Lee & L. D'Amico (Eds.), Learning and becoming in practice: The International Conference of the Learning Sciences (ICLS) 2014 (Vol. 2, pp. 919–926). International Society of the Learning Sciences. https://doi.org/10.22318/icls2014.919
  • Box, G. E. (1979). Robustness in the strategy of scientific model building. In R. L. Launer & G. N. Wilkinson (Eds.), Robustness in statistics (pp. 201–236). Elsevier. https://doi.org/10.1016/B978-0-12-438150-6.50018-2
  • Carley, K. (1986). Knowledge acquisition as a social phenomenon. Instructional Science, 14(3), 381–438. https://doi.org/10.1007/BF00051829
  • Carley, K. (1991). A theory of group stability. American Sociological Review, 56(3), 331–354. https://doi.org/10.2307/2096108
  • Carley, K., Martin, M. K., & Hirshman, B. R. (2009). The etiology of social change. Topics in Cognitive Science, 1(4), 621–650. https://doi.org/10.1111/j.1756-8765.2009.01037.x
  • Clancey, W. J. (2008). Scientific antecedents of situated cognition. In Cambridge handbook of situated cognition (pp. 11–34). Cambridge University Press. https://doi.org/10.1017/CBO9780511816826.002
  • Cooper, R. P. (2019). Multidisciplinary flux and multiple research traditions within cognitive science. Topics in Cognitive Science, 11(4), 869–879. https://doi.org/10.1111/tops.12460
  • diSessa, A. A. (1993). Toward an epistemology of physics. Cognition & Instruction, 10(2–3), 105–225. https://doi.org/10.1080/07370008.1985.9649008
  • diSessa, A. A. (2018). A friendly introduction to “Knowledge in Pieces”: Modeling types of knowledge and their roles in learning. In Invited Lectures from the 13th International Congress on Mathematical Education (pp. 65–84). https://doi.org/10.1007/978-3-030-15636-7_11
  • Doroudi, S. (2020). The bias-variance tradeoff: How data science can inform educational debates. AERA Open, 6(4), 233285842097720. https://doi.org/10.1177/2332858420977208
  • Doroudi, S. (2022). The intertwined histories of artificial intelligence and education. International Journal of Artificial Intelligence in Education, 1–44. https://doi.org/10.1007/s40593-022-00313-2
  • Doroudi, S., & Rastegar, S. A. (2023). The bias–variance tradeoff in cognitive science. Cognitive Science, 47(1), e13241. https://doi.org/10.1111/cogs.13241
  • Forbus, K. D. (2010). AI and cognitive science: The past and next 30 years. Topics in Cognitive Science, 2(3), 345–356. https://doi.org/10.1111/j.1756-8765.2010.01083.x
  • Gardner, H. (1987). The mind’s new science: A history of the cognitive revolution. Basic books.
  • Gentner, D. (2019). Cognitive science is and should be pluralistic. Topics in Cognitive Science, 11(4), 884–891. https://doi.org/10.1111/tops.12459
  • Goel, A. (2022). Looking back, looking ahead: Symbolic versus connectionist ai. AI Magazine, 42(4), 83–85. https://doi.org/10.1609/aimag.v42i4.15111
  • Greeno, J. G., & Moore, J. L. (1993). Situativity and symbols: Response to vera and Simon. Cognitive Science, 17(1), 49–59. https://doi.org/10.1207/s15516709cog1701_3
  • Hennig, C. (2003). How wrong models become useful—and correct models become dangerous. In M. Schader, W. Gaul & M. Vichi (Eds.), Between data science and applied data analysis (pp. 235–243). Springer. https://doi.org/10.1007/978-3-642-18991-3_27
  • Hennig, C. (2010). Mathematical models and reality: A constructivist perspective. Foundations of Science, 15(1), 29–48. https://doi.org/10.1007/s10699-009-9167-x
  • Hof, B. (2021). The turtle and the mouse: How constructivist learning theory shaped artificial intelligence and educational technology in the 1960s. History of Education, 50(1), 93–111. https://doi.org/10.1080/0046760X.2020.1826053
  • Hutchins, E. (1990). The technology of team navigation. In J. Galegher, R. E. Kraut & C. Egido (Eds.), Intellectual teamwork: Social and technological foundations of cooperative work (Vol. 1, pp. 191–220). https://doi.org/10.4324/9781315807645-10
  • Hutchins, E., & Hazlehurst, B. (1991). Learning in the cultural process. In C. G. Langton, C. Taylor, J. D. Farmer & S. Rasmussen (Eds.), Artificial life II. SFI studies in the sciences of complexity (Vol. 10, pp. 689–706). Addison Wesley.
  • Hutchins, E., & Hazlehurst, B. (1995). How to invent a lexicon: The development of shared symbols in interaction. In N. Gilbert & R. Conte (Eds.), Artificial societies: The computer simulation of social life (pp. 157–189). UCL Press. https://doi.org/10.4324/9780203993699
  • Jacobson, M. J., Kapur, M., & Reimann, P. (2016). Conceptualizing debates in learning and educational research: Toward a complex systems conceptual framework of learning. Educational Psychologist, 51(2), 210–218. https://doi.org/10.1080/00461520.2016.1166963
  • Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), 11–34. https://doi.org/10.1207/s15327809jls1501_4
  • Johnson, G. M. (2021). Algorithmic bias: On the implicit biases of social technology. Synthese, 198(10), 9941–9961. https://doi.org/10.1007/s11229-020-02696-y
  • Johnson, M. (1989). Embodied knowledge. Curriculum Inquiry, 19(4), 361–377. https://doi.org/10.1080/03626784.1989.11075338
  • Kolodner, J. L. (1991). The Journal of the Learning Sciences: Effecting changes in education. Journal of the Learning Sciences, 1(1), 1–6. https://doi.org/10.1207/s15327809jls0101_1
  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815355
  • Marcus, G. (2020). The next decade in AI: Four steps towards robust artificial intelligence. arXiv preprint arXiv:2002.06177.
  • Martınez-Plumed, F., Loe, B. S., Flach, P., O hEigeartaigh, S., Vold, K., & Hernández-Orallo, J. (2018). The facets of artificial intelligence: A framework to track the evolution of AI. In J. Lang (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp. 5180–5187). https://doi.org/10.24963/ijcai.2018/718
  • McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11–38. https://doi.org/10.1111/j.1756-8765.2008.01003.x
  • McClelland, J. L., Rumelhart, D. E. PDP Research Group. (1986). Parallel distributed processing (Vol. 1). MIT Press. https://doi.org/10.7551/mitpress/5236.001.0001
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
  • Minsky, M. (1988). The society of mind. Simon and Schuster. https://doi.org/10.1016/0004-3702(91)90036-J
  • Minsky, M. (1991). Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Magazine, 12(2), 34–34. https://doi.org/10.1609/aimag.v12i2.894
  • Nasir, N. S., Lee, C. D., Pea, R., & McKinney de Royston, M. (2021). Rethinking learning: What the interdisciplinary science tells us. Educational Researcher, 50(8), 557–565. https://doi.org/10.3102/0013189X211047251
  • Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press. https://ai.stanford.edu/~nilsson/QAI/qai.pdf
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
  • Pask, G. (1961). An approach to cybernetics. Hutchinson.
  • Pask, G. (1965). The cybernetics of human learning and performance: A guide to theory and research. Hutchinson Educational.
  • Pask, G. (1975). Conversation, cognition and learning: A cybernetic theory and methodology. Elsevier.
  • Pask, G., & Kopstein, F. F. (1977). Teaching machines revisited in the light of conversation theory. Educational Technology, 17(10), 38–41.
  • Perconti, P., & Plebe, A. (2020). Deep learning and cognitive science. Cognition, 203, 104365. https://doi.org/10.1016/j.cognition.2020.104365
  • Pezzulo, G., Barsalou, L. W., Cangelosi, A., Fischer, M. H., McRae, K., & Spivey, M. J. (2013). Computational grounded cognition: A new alliance between grounded cognition and computational modeling. Frontiers in Psychology, 3, 612. https://doi.org/10.3389/fpsyg.2012.00612
  • Salomon, G. (1993). Distributed cognitions: Psychological and educational considerations. Cambridge University Press.
  • Sawyer, R. K. (2005). Introduction: The new science of learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (1st ed., pp. 1–16). Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.002
  • Schöner, G. (2008). Dynamical systems approaches to cognition. In Cambridge handbook of computational psychology (pp. 101–126). Cambridge University Press. https://doi.org/10.1017/CBO9780511816772.007
  • Scott, B. (2001a). Gordon pask’s contributions to psychology. Kybernetes, 30(7/8), 891–901. https://doi.org/10.1108/EUM0000000005915
  • Scott, B. (2001b). Gordon pask’s conversation theory: A domain independent constructivist model of human knowing. Foundations of Science, 6(4), 343–360. https://doi.org/10.1023/A:1011667022540
  • Self, J. (2016). The birth of IJAIED. International Journal of Artificial Intelligence in Education, 26(1), 4–12. https://doi.org/10.1007/s40593-015-0040-5
  • Sun, R., & Alexandre, F. (1997). Connectionist-symbolic integration: From unified to hybrid approaches. Psychology Press.
  • Synced. (2018). The AAAI 2018 keyword is “learning”. https://medium.com/syncedreview/the-aaai-2018-keyword-is-learning-d0aefcccde3a
  • von Glasersfeld, E. (1984). An introduction to radical constructivism. In P. Watzlawick (Ed.), The invented reality (pp. 17–40). Norton.
  • von Glasersfeld, E. (1992). Declaration of the American Society for Cybernetics. http://www.vonglasersfeld.com/065

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