Toward a Theory of Personalized Learning Communities

  • Eric HamiltonEmail author
  • Martine Jago


This chapter proposes a theory for the design and dynamics of personalized learning communities (PLCs), with examples drawn from, but not limited to, common classroom environments. The theory is meant to draw on an eclectic set of frameworks and to supplement and inform other approaches to learning community design. It relies on 11 principles that each distinctively contribute to PLC design. The theory suggests that the principles interact with one another in synergistic and self-propagating ways that blur the differences between cause and effect in classroom dynamics. An example that blends five technologies in a single platform illustrates the higher-order interactions that can underlie the development of personalized learning communities.


Personalized learning communities Design principles Systems thinking Technology affordances Interactional bandwith Agents Self-regulation Modeling 


  1. Adelson, B. (2003). Issues in scientific creativity: insight, perseverance and personal technique - Profiles of the 2002 Franklin Institute Laureates. Journal of The Franklin Institute, 340(3), 163-189.CrossRefGoogle Scholar
  2. Azevedo, R., Guthrie, J. T., & Seibert, D. (2004). The role of self-regulated learning in fostering students’conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30(1), 87-111.CrossRefGoogle Scholar
  3. Barrett, L. F. & Barrett, D. J. (2001). An introduction to computerized experience sampling in psychology. Social Science Computer Review, 19(2), 175-185.CrossRefGoogle Scholar
  4. Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-on-one tutoring. Educational Researcher, 13, 4-16.Google Scholar
  5. Bransford, J. D., Brown, A. L., Cocking, R. R., & Donovan, S. (eds). (2000). How people learn: Brain, mind, experience, and school (expanded edition). Washington, DC: National Academy Press.Google Scholar
  6. Buckley, B. C., Gobert, J. D., Kindfield, A. C. H., Horwitz, P., Tinker, R. F., Gerlits, B., et al. (2004). Model-based teaching & learning with BioLogica: What do they learn? How do they learn? How do we know? Journal of Science Education & Technology, 13(1), 23-41.CrossRefGoogle Scholar
  7. Bull, S., & Kay, J. (2005, July 18-22, 2005). A framework for designing and analysing open learner modelling. Paper presented at the 12th International Conference on Artificial Intelligence in Education, Amsterdam, the Netherlands.Google Scholar
  8. Cacioppo, J. T., Norris, C. J., Decety, J., Monteleone, G., & Nusbaum, H. (2009). In the eye of the beholder: Individual differences in perceived social isolation predict regional brain activation to social stimuli. Journal of Cognitive Neuroscience, 21(1), 83-92.CrossRefGoogle Scholar
  9. Chen, H. L., Lattuca, L. R., & Hamilton, E. R. (2008). Conceptualizing engagement: Contributions of faculty to student engagement in engineering. Journal for Engineering Education, 97(3), 339-353.Google Scholar
  10. Chuansheng, C., Kasof, J., Himsel, A., Dmitaieva, J., Qi, D., & Gui, X. (2005). Effects of explicit instruction to be creative across domains and cultures. Journal of Creative Behavior, 39(2), 89-110.Google Scholar
  11. Clark, R. C. & Mayer, R. E. (2003). E-Learning and the science of instruction: proven guidelines for consumers and designers of multimedia learning. San Francisco, CA: Jossey-Bass/Pfeiffer.Google Scholar
  12. Clement, J. J. & Rea-Ramirez, M. A. (2007). Model based learning and instruction in science. Science and Education, 16(7-8), 647-652.CrossRefGoogle Scholar
  13. Corno, L. Y. N. (2008). On teaching adaptively. Educational Psychologist, 43(3), 161-173.CrossRefGoogle Scholar
  14. Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. New York: Harper Collins.Google Scholar
  15. Diefes-Dex. (2007). Collaborative research: Impact of model-eliciting activities on engineering teaching and learning: National Science Foundation Grant DUE-0717865 to Purdue University.Google Scholar
  16. English, L. D., Fox, J. L., & Watters, J. J. (2005). Problem posing and solving with mathematical modeling. Teaching Children Mathematics, 12(3), 156-163.Google Scholar
  17. Frank, M. J., Doll, B. B., Oas-Terpstra, J., & Moreno, F. (2009). Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation. Nature Neuroscience, 12(8), 1062-1068.CrossRefGoogle Scholar
  18. Hamilton, E. (2005). Affective composites: Autonomy and proxy in pedagogical agent networks. In J. Tao, J. Tan & R. E. Picard (Eds.), Affective computing and intelligent interaction (ACII2005) (Vol. 3784, pp. 898-906). Berlin: Springer Lecture Notes in Computer Science.CrossRefGoogle Scholar
  19. Hamilton, E. (2007a). Emerging metaphors and constructs from pedagogical agent networks. Educational Technology (Special Issue, A. Baylor editor), 47(1).Google Scholar
  20. Hamilton, E. (2007b). What changes are occurring in the kind of problem-solving situations where mathematical thinking is needed beyond school? In R. Lesh, E. Hamilton & J. Kaput (Eds.), Foundations for the future in mathematics education. Mahweh, NJ: Lawrence Erlbaum Associates.Google Scholar
  21. Hamilton, E., & Harding, N. (2008). IES grant: Agent and Library Augmented Shared Knowledge Areas (ALASKA). Institute for Education Sciences Award 305A080667.Google Scholar
  22. Hamilton, E., & Hurford, A. (2007). Combining Collaborative Workspaces with Tablet Computing: Research in Learner Engagement and Conditions of Flow. Proceedings of the 37th ASEE/IEEE Frontiers in Education Conference, Milwaukee, WI, pages C3-C8.Google Scholar
  23. Hamilton, E., Lesh, R., & Lester, F. (2008). Model-eliciting activities (MEAs) as a bridge between engineering education research and mathematics education research. Advances in Engineering Education, 1(2), 1-25.Google Scholar
  24. Hatano, G., & Inagaki, K. (2000, April). Practice makes a difference: Design principles for adaptive expertise. Paper Presented at the Annual Meeting of the American Education Research Association, New Orleans, LO.Google Scholar
  25. Hjalmarson, M. A., Cardella, M., & Adams, R. (2007). Uncertainty and iteration in design tasks for engineering students. In R. Lesh, E. Hamilton & J. Kaput (Eds.), Foundations for the future in mathematics education. Mahweh, NJ: Lawrence Erlbaum Associates.Google Scholar
  26. Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learn about complex systems. Journal of the Learning Sciences, 9(3), 247-298.CrossRefGoogle Scholar
  27. Hsiang, C. (2006). Digitization of the experience sampling method: transformation, implementation, and assessment. Social Science Computer Review, 24(1), 106-118.CrossRefGoogle Scholar
  28. Kay, J. A. (2000). Accretion representation for scrutable student modelling. Paper presented at the Proceedings of the 5th International Conference on Intelligent Tutoring Systems, Montreal, Canada.Google Scholar
  29. Kolodner, J. L., Camp, J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., et al. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: putting learning by design(TM) into practice. The Journal of the Learning Sciences, 12(4), 495-547.CrossRefGoogle Scholar
  30. Latz, A. O., Speirs Neumeister, K. L., Adams, C. M., & Pierce, R. L. (2009). Peer coaching to improve classroom differentiation: Perspectives from project CLUE. Roeper Review, 31(1), 27-39.CrossRefGoogle Scholar
  31. Lesh, R. (2006). Modeling students modeling abilities: The teaching and learning of complex systems in education. Journal of the Learning Sciences, 15(1), 45-52.CrossRefGoogle Scholar
  32. Lesh, R., & Doerr, H. (Cartographer). (2003). Beyond constructivism: A models & modeling perspective on mathematics teaching, learning, and problems solving. Mahweh, NJ: Lawrence Erlbaum Associates.Google Scholar
  33. Lesh, R., Hamilton, E., & Kaput, J. (2007). Foundations for the future in mathematics education. Mahweh, NJ: Lawrence Erlbaum Associates.Google Scholar
  34. Lesh, R., Hoover, M., Hole, B., Kelly, A., & Post, T. (2000). Principles for developing thought revealing activities for students and teachers. In A. Kelly & R. Lesh (Eds.), The handbook of research design in mathematics and science education. Mahweh, NJ: Lawrence Erlbaum Associates.Google Scholar
  35. Lesh, R., Middleton, J. A., Caylor, E., & Gupta, S. (2008). A science need: Designing tasks to engage students in modeling complex data. Educational Studies in Mathematics, 68(2), 113-130.CrossRefGoogle Scholar
  36. Lesh, R. & Yoon, C. (2004). Evolving communities of mind-In which development involves several interacting and simultaneous developing strands. Mathematical Thinking & Learning, 6(2), 205-226.CrossRefGoogle Scholar
  37. Lesh, R., Yoon, C., & Zawojewski, J. (2007). John Dewey revisited—Making mathematics practical versus making practice mathematical. In R. Lesh, E. Hamilton & J. Kaput (Eds.), Foundations for the future in mathematics education. Mahweh, NJ: Lawrence Erlbaum Associates.Google Scholar
  38. Mehlhorn, J. (2006). Fostering group creativity. Scientific American Mind, 17(4), 78-79.CrossRefGoogle Scholar
  39. Miller, R., & Olds, M. (2007). Collaborative research: Impact of model-eliciting activities on engineering teaching and learning: National Science Foundation Grant DUE-0717862 to the Colorado School of Mines.Google Scholar
  40. Nakamura, J. & Csikszentmihalyi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89-105). Oxford: Oxford University Press.Google Scholar
  41. National Academy of Engineering. (2005). The engineer of 2020: Visions of engineering in the new century. Washington, DC: National Academy of Engineering.Google Scholar
  42. National Science Foundation. (2008). CreativeIT funding program - NSF 08572. Retrieved July 1, 2009 from
  43. Roschelle, J., Tatar, D., Shechtman, N., & Knudsen, J. (2008). The role of scaling up research in designing for and evaluating robustness. Educational Studies in Mathematics, 68(2), 149-170.CrossRefGoogle Scholar
  44. Roth, W.-M. (2007). Mathematical modeling ‘in the Wild’: A case of hot cognition. In R. Lesh, E. Hamilton & J. Kaput (Eds.), Foundations for the future in mathematics education. Mahweh, NJ: Lawrence Erlbaum Associates.Google Scholar
  45. Shuman, L., & Besterfield-Sacre, M. (2007). Collaborative research: Impact of model-eliciting activities on engineering teaching and learning: National Science Foundation Grant DUE-0717861 to the University of Pittsburgh.Google Scholar
  46. Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153-189.CrossRefGoogle Scholar
  47. Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational & Psychological Measurement, 69(3), 493-525.CrossRefGoogle Scholar
  48. Swee Fong, N. & Lee, K. (2009). The model method: Singapore children’s tool for representing and solving algebraic word problems. Journal for Research in Mathematics Education, 40(3), 282-313.Google Scholar
  49. Tarquin, K. & Cook-Cottone, C. (2008). Relationships among aspects of student alienation and self concept. School Psychology Quarterly, 23(1), 16-25.CrossRefGoogle Scholar
  50. Thomas, D. & Brown, J. S. (2009). Why virtual worlds can matter. International Journal of Learning and Media, 1(1), 37-49.CrossRefGoogle Scholar
  51. Wagner, C. (2008). Learning experience with virtual worlds. Journal of Information Systems Education, 19(3), 263-266.Google Scholar
  52. Weigel, M., James, C., & Gardner, H. (2009). Learning: Peering backward and looking forward in the digital era. International Journal of Learning and Media, 1(1), 1-18.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Graduate School of Education and Psychology, Pepperdine UniversityLos AngelesUSA

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