Diffusion of pedagogical innovations as a complex adaptive process - Agent-based modeling as research method

15th International Conference on Computers in Education: Supporting Learning Flow through Integrative Technologies, ICCE 2007

Volumn , Issue , 2007, Pages 67-74

  Huang, Jun Song ^a   Kapur, Manu ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Agent based modeling; Complex adaptive process; Diffusion of innovation; Pedagogical innovations; Research methods

Indexed keywords

ADAPTIVE PROCESS; AFFORDANCES; AGENT-BASED MODELING; COMPLEMENTARY METHODS; COMPLEX ADAPTIVE PROCESS; DECISION MAKING PROCESS; DIFFUSION OF INNOVATION; DIFFUSION PATTERNS; DIFFUSION PROCESS; EDUCATIONAL CONTEXT; EMERGENT PROPERTY; INNOVATION DIFFUSION; RESEARCH METHODS;

COMPUTATIONAL METHODS; EDUCATION; INNOVATION; RESEARCH;

DIFFUSION;

EID: 84863178145     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (40)

1. Looi, C. K., Lim, W. Y., Koh, T. S., & Hung, D. (2005). Systemic Innovations and the Role of Change-Technology: Issues of Sustainability and Generalizability. Paper presented at the ICCE2005.
2. Surry, D. W., & Ely, D. P. (2001). Adoption, diffusion, implementation, and institutionalization of educational innovations. Trends & Issues in Instructional Design and Technology. Prentice-Hall, 183-193.
3. Fullan, M. (1994). Coordinating top-down and bottom-up strategies for educational reform. In System Reform: Perspectives on Personalizing Education.
4. Luehmann, A. L. (2002). Powerful Hidden Forces Affecting Teachers' Appraisal and Adoption of Innovative Technology-Rich Curricular Supports for Secondary Water Quality Learning. Paper presented at the American Educational Research Association, New Orleans, LA.
5. Rogers, E. M. (2003). Diffusion of Innovations: Simon and Schuster.
6. Valente, T. W. (1996). Network models of the diffusion of innovations. Cresskill, New Jersey: Hampton.
7. Goldstone, R. L., & Janssen, M. A. (2005). Computational models of collective behavior. TRENDS in Cognitive Sciences, 9(9).
8. Kauffman, S. A. (1995). At Home in the Universe: The Search for Laws of Self-organization and Complexity: Oxford University Press.
9. Holland, J. H. (1995). Hidden Order: How Adaption Builds Complexity: Perseus Books.
10. Eisenhardt, K. M. (1989). Agency theory: An assessment and review. The Academy of Management Review, 14(1), 57-74.
11. Bar-Yam, Y. (2003). Dynamics of Complex Systems: Westview Press.
12. Bass, M. F. (1969). A new product growth model for consumer durables. Management Science, 15(5), 215-227.
13. Tabulawa, R. (1998). Teachers' perspectives on classroom practice in Botswana: implications for pedagogical change. International Journal of Qualitative Studies in Education, 11(2), 249-268.
14. Deffuant, G., Huet, S., & Amblard, F. (2005). An individual-based model of innovation diffusion mixing social value and individual benefit. American Journal of Sociology, 110(4), 1041-1069.
15. Hall, G. E., Hord, S., Huling-Austin, L., & Rutherford, W. L. (1987). Taking charge of change. Alexandria, VA: Association for Supervision and Curriculum Development.
16. Zhao, Y., Lei, J., & Frank, K. A. (2006). The Social Life of Technology: An Ecological Analysis of Technology Diffusion in Schools. Pedagogies: An International Journal, 1(2), 135-149.
17. Wejnert, B. (2002). Integrating Models of Diffusion of Innovations: A Conceptual Framework. Annual Review of Sociology, 297-327.
18. Anderson, P. W. (1972). More is different: broken symmetry and the nature of the hierarchical structure of science. Science, 177(4047), 393-396.
19. Epstein, J. M., & Axtell, R. L. (1996). Growing Artificial Societies: Social Science from the Bottom Up: Brookings Institution Press.
20. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
21. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478.
22. Hu, P. J. H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: a longitudinal study. Information & Management, 41(2), 227-241.
23. Snedecor, G. W. (1989). Statistical Methods: Iowa State Press.
24. Basilevsky, A. (1995). Statistical factor analysis and related methods: Theory and applications. Technometrics, 37(2).
25. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Quarterly, 23(2), 183-213.
26. Fisher, R. (1955). Statistical methods and scientific induction. Journal of the Royal Statistical Society. Series B (Methodological), 17(1), 69-78.
27. Baudisch, A. F., & Grupp, H. (2006). Evaluating the market potential of innovations: A structured survey of diffusion models [Electronic Version]. Retrieved May 07, 2007 from http://www.wiwi.uni-jena.de.
28. Mahajan, V., & Wind, Y. (1986). Innovation diffusion models of new product acceptance: Ballinger.
29. Meade, N., & Islam, T. (2006). Modelling and forecasting the diffusion of innovation-A 25-year review. International Journal of Forecasting, 22, 519-545.
30. Wasserman, S., & Faust, K. (1994). Social Network Analysis: methods and applications: Cambridge.
31. Creswell, D. J. W. (2003). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches: Sage
32. Eidelson, R. J. (1997). Complex adaptive systems in the behavioral and social sciences. Review of General Psychology, 1(1), 42-71.
33. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and Agent-Based Modeling. Annual Review of Sociology, 28, 143-167.
34. Axelrod, R. (2006). Advancing the art of simulation in the social sciences. In J. P. Rennard (Ed.), Handbook of Research on Nature-inspired Computing for Economics and Management. Hersey: Idea.
35. Jackson, E. A. (1996). The Second Metamorphosis of Science: A Second View: Working Paper 96-05-039. Santa Fe Institute: New Mexico.
36. Nowak, A. (2004). Dynamical minimalism: Why less is more in psychology. Personality and Social Psychology Review, 8(2), 183.
37. Kapur, M., Hung, D., Jacobson, M., Voiklis, J., Kinzer, C. K., & Chen, V. (2007). Emergence of Learning in Computer-Supported, Large-Scale Collective Dynamics: A Research Agenda. Paper presented at the CSCL2007.
38. Balci, O. (1997). Principles of simulation model validation, verification, and testing. Transactions of the Society for Computer Simulation International, 14(1), 3-12.
39. Brenner, T., & Werker, C. (2006). A Practical Guide to Inference in Simulation Models [Electronic Version]. Papers on Economics & Evolution. Retrieved May 07, 2007 from ftp://papers.econ.mpg.de/.
40. Sargent, R. G. (2005). Verification and validation of simulation models. Winter Simulation Conference, 2005 Proceedings of the, 130-143.