Learning by Modeling (LbM): Understanding Complex Systems by Articulating Structures, Behaviors, and Functions

Understanding the behavior of complex systems has become a focal issue for scientists in a wide range of disciplines. Making sense of a complex system should require that a student construct a network of concepts and principles about the learning complex phenomena. This paper describes part of a project about Learning-by-Modeling (LbM). 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. In this study we investigated the effect of different modes of involvement in exploring scientific phenomena using computer simulation tools, on students’ mental model from the perspective of structure, behaviour 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.

@article{Hashem2013,
  title     = {Learning by Modeling (LbM): Understanding Complex Systems by Articulating Structures, Behaviors, and Functions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {4},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Kamel Hashem and David Mioduser},
  doi       = {10.14569/IJACSA.2013.040414},
  url       = {https://doi.org/10.14569/IJACSA.2013.040414}
}