Promoting Complex Systems Learning through the use of Computer Modeling
This paper describes part of a project about Learningby-
Modeling (LbM). Studying complex systems is increasingly
important in teaching and learning many science domains. Many
features of complex systems make it difficult for students to develop
deep understanding. Previous research indicates that involvement
with modeling scientific phenomena and complex systems can play a
powerful role in science learning. Some researchers argue with this
view indicating that models and modeling do not contribute to
understanding complexity concepts, since these increases the
cognitive load on students. This study will investigate the effect of
different modes of involvement in exploring scientific phenomena
using computer simulation tools, on students- mental model from the
perspective of structure, behavior and function. Quantitative and
qualitative methods are used to report about 121 freshmen students
that engaged in participatory simulations about complex phenomena,
showing emergent, self-organized and decentralized patterns. Results
show that LbM plays a major role in students' concept formation
about complexity concepts.
[1] Blikstein, P., and Wilensky, U. (2005). Less is more: agent-based
simulation as a powerful learning tool in materials science. Proceedings
of the IV International Joint Conference on AAMAS. Utrecht, Holland.
[2] Buckley, C. B., Gobert, J., Kindfield, A., Horwitz, P., Tinker, R.,
Gerlits, B., et al. (2004). Model-Based Teaching and Learning with
BioLogica: What do they learn? How do they learn? How do we know?
Journal of Science Education and Technology , 13 (1), 23-41.
[3] Chen, D., and Stroup, W. (1993). General system theory: Toward a
conceptual framework for science and technology education for all.
Journal of Science Education and Technology , 2 (3), 447-459.
[4] Chi, M. T. (2005). Commonsense conceptions of emergent processes:
why some misconceptions are robust? The Journal of the Learning
Sciences, 14 (2), 161-199.
[5] Gilbert, K. J., and Boulter, J. C. (Eds.). (2000). Developing models in
science education. Dordrecht, Holland: Kluwer Academic Publishers.
[6] Gobert, D. J., and Buckley, C. B. (2000). Introduction to model-based
teaching and learning in science education. International Journal of
Science Education, 22 (9), 891-894.
[7] Gobert, J. (2003). Harnessing technology to support on-line model
building and peer collaboration.
[8] Hashem, K., and Mioduser, D. (2011). The Contribution of Learning by
Modeling (LbM) to Students' Understanding of Complexity Concepts.
International Journal of e-Education, e-Business, e-Management and e-
Learning (IJEEEE , 1 (2), 151-155.
[9] Hmelo-Silver, C., and Pfeffer, M. G. (2004). Comparing expert and
novice understanding of a complex system from the perspective of
structures, behaviors, and functions. Cognitive Science , 28 (1), 127-138.
[10] Jacobson, M. (2001). Problem solving, cognition, and complex systems:
Differences between experts and novices. Complexity , 6 (3), 41-49.
[11] Jacobson, M., and 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.
[12] Levy, S., and Wilensky, U. (2005). An analysis of student patterns of
exploration with NetLogo models embedded in the connected chemistry
environment. Proceedings of the annual meeting of the American
Educational Research Association. Montreal, CA.
[13] Louca, L., and Constantinou, C. (2003). The use of computer-based
microworlds for developing modeling skills in physical science: an
example from light. International Journal of Science Education .
[14] Norman, D. A. (1983). Some observations on mental models. In D.
Gentner, & A. L. Stevens (Eds.), Mental models. Hillsdale, NJ: Erlbaum.
[15] Resnick, M. (1994). Changing the centralized mind. Cambridge, MA:
MIT press.
[16] Resnick, M. (1996). Beyond the centralized mindset. Journal of the
Learning Sciences , 5 (1), 1-22.
[17] Resnick, M., and Wilensky, U. (1998). Diving into Complexity:
Developing probabilistic decentralized thinking through role-playing
activities. Journal of Learning Sciences , 7 (2), 153-172.
[18] Stieff, M., and Wilensky, U. (2003). Connected chemistry-incorporating
interactive simulations into the chemistry classroom. Journal of Science
Education and Technology, 12 (3), 285-302.
[19] Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R.,
Gray, S., et al. (2011). Understanding Complex Natural Systems by
Articulating Structure-Behavior-Function models. Educational
Technology & Society, 14 (1), 66-81.
[20] Wilensky, U., and Resnick, M. (1999). Thinking in levels: A dynamic
systems approach to making sense of the world. Journal of Science
Education and Technology, 8 (1), 3-19.
[21] Yehezkel, C., Ben-Ari, M., and Dreyfus, T. (2005). Computer
architecture and mental models. ACM , 101-105.
[1] Blikstein, P., and Wilensky, U. (2005). Less is more: agent-based
simulation as a powerful learning tool in materials science. Proceedings
of the IV International Joint Conference on AAMAS. Utrecht, Holland.
[2] Buckley, C. B., Gobert, J., Kindfield, A., Horwitz, P., Tinker, R.,
Gerlits, B., et al. (2004). Model-Based Teaching and Learning with
BioLogica: What do they learn? How do they learn? How do we know?
Journal of Science Education and Technology , 13 (1), 23-41.
[3] Chen, D., and Stroup, W. (1993). General system theory: Toward a
conceptual framework for science and technology education for all.
Journal of Science Education and Technology , 2 (3), 447-459.
[4] Chi, M. T. (2005). Commonsense conceptions of emergent processes:
why some misconceptions are robust? The Journal of the Learning
Sciences, 14 (2), 161-199.
[5] Gilbert, K. J., and Boulter, J. C. (Eds.). (2000). Developing models in
science education. Dordrecht, Holland: Kluwer Academic Publishers.
[6] Gobert, D. J., and Buckley, C. B. (2000). Introduction to model-based
teaching and learning in science education. International Journal of
Science Education, 22 (9), 891-894.
[7] Gobert, J. (2003). Harnessing technology to support on-line model
building and peer collaboration.
[8] Hashem, K., and Mioduser, D. (2011). The Contribution of Learning by
Modeling (LbM) to Students' Understanding of Complexity Concepts.
International Journal of e-Education, e-Business, e-Management and e-
Learning (IJEEEE , 1 (2), 151-155.
[9] Hmelo-Silver, C., and Pfeffer, M. G. (2004). Comparing expert and
novice understanding of a complex system from the perspective of
structures, behaviors, and functions. Cognitive Science , 28 (1), 127-138.
[10] Jacobson, M. (2001). Problem solving, cognition, and complex systems:
Differences between experts and novices. Complexity , 6 (3), 41-49.
[11] Jacobson, M., and 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.
[12] Levy, S., and Wilensky, U. (2005). An analysis of student patterns of
exploration with NetLogo models embedded in the connected chemistry
environment. Proceedings of the annual meeting of the American
Educational Research Association. Montreal, CA.
[13] Louca, L., and Constantinou, C. (2003). The use of computer-based
microworlds for developing modeling skills in physical science: an
example from light. International Journal of Science Education .
[14] Norman, D. A. (1983). Some observations on mental models. In D.
Gentner, & A. L. Stevens (Eds.), Mental models. Hillsdale, NJ: Erlbaum.
[15] Resnick, M. (1994). Changing the centralized mind. Cambridge, MA:
MIT press.
[16] Resnick, M. (1996). Beyond the centralized mindset. Journal of the
Learning Sciences , 5 (1), 1-22.
[17] Resnick, M., and Wilensky, U. (1998). Diving into Complexity:
Developing probabilistic decentralized thinking through role-playing
activities. Journal of Learning Sciences , 7 (2), 153-172.
[18] Stieff, M., and Wilensky, U. (2003). Connected chemistry-incorporating
interactive simulations into the chemistry classroom. Journal of Science
Education and Technology, 12 (3), 285-302.
[19] Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R.,
Gray, S., et al. (2011). Understanding Complex Natural Systems by
Articulating Structure-Behavior-Function models. Educational
Technology & Society, 14 (1), 66-81.
[20] Wilensky, U., and Resnick, M. (1999). Thinking in levels: A dynamic
systems approach to making sense of the world. Journal of Science
Education and Technology, 8 (1), 3-19.
[21] Yehezkel, C., Ben-Ari, M., and Dreyfus, T. (2005). Computer
architecture and mental models. ACM , 101-105.
@article{"International Journal of Business, Human and Social Sciences:59273", author = "Kamel Hashem and David Mioduser", title = "Promoting Complex Systems Learning through the use of Computer Modeling", abstract = "This paper describes part of a project about Learningby-
Modeling (LbM). Studying complex systems is increasingly
important in teaching and learning many science domains. Many
features of complex systems make it difficult for students to develop
deep understanding. Previous research indicates that involvement
with modeling scientific phenomena and complex systems can play a
powerful role in science learning. Some researchers argue with this
view indicating that models and modeling do not contribute to
understanding complexity concepts, since these increases the
cognitive load on students. This study will investigate the effect of
different modes of involvement in exploring scientific phenomena
using computer simulation tools, on students- mental model from the
perspective of structure, behavior and function. Quantitative and
qualitative methods are used to report about 121 freshmen students
that engaged in participatory simulations about complex phenomena,
showing emergent, self-organized and decentralized patterns. Results
show that LbM plays a major role in students' concept formation
about complexity concepts.", keywords = "Complexity, Educational technology, Learning by modeling, Mental models", volume = "5", number = "11", pages = "1598-6", }
