Abstract:

Agent-based modeling (ABM) offers learners the opportunity to actively engage in complex modeling situations. These range from the physical and natural sciences to those topics which are relevant in today’s society under the auspice of a variety of socio-economic or cultural happenings. Minimal user interaction is required, as the simulation or game is designed and the outcome of the data on human behavior and logical thought processes. This encourages learners to work collaboratively in a multi-player gaming forum and to realistically apply scientific principles and properties in a low-threat matrix environment. The use of ABM within educational games key component in improving Critical Thinking (CT) and Cognitive Load (CL) abilities for a more realistic learning environment.

ABM propounds a practical application of identifying active entities by the modeler, of agents while defining the agents’ behavior(s). The agents, in this learning forum, were the learner/users of the simulation game, while the behavior was the established connection(s) between scientific concepts and phenomena and the practical application(s) therein. ABM is a fairly new approach to the modeling of complex systems, in which autonomous ‘agents’ or users abide by ‘behaviors’ or rules of engagement. The users or agents interact with one another, thus influencing their behavior(s), therefore altering the outcome(s) of the simulation game. This in turn creates a more realistic view of general behavioral practices. These practices give rise to patterns and structural attributes that cannot be expressly or explicitly programmed into the game itself.

The use of imagery and iconic representation of scientific concepts is a key component in improving Critical Thinking (CT) skills while maintaining optimal Cognitive Load (CL) within higher education STEM learners. Laboratory experiences are a vital component within science education, while rote traditional lab experiments are currently not addressing inquiry nor linking with educational technologies. Instructional approaches based on active discovery and problem-based learning using digital games is becoming more commonplace in today’s educational forum. Opportunities to alternatively assess learning and evaluate comprehension in a digital learning environment are supportive from both a theoretical perspective and an empirical research perspective. Using educational games for assessment not only measures previously outlines learning objectives and goals, but allows learners to measure their cognitive load abilities in these scenarios.

The research performed was a causal-comparative quantitative study with 150 learners enrolled at a two-year community college, to determine the effects of Agent-Based Modelling on learner behavior within science education. Data collection involved a quantitative analysis of pre/post-laboratory experiment surveys that included using a Repeated Measures ANOVA test for ABM or non-ABM for cognitive load and critical thinking modalities. By studying the manner in which learners comprehend information and reducing their cognitive load while conducting scientific experiments in Virtual Learning Environments (VLEs), we are provided with the information required to structure pedagogical changes and appropriate technology resources in applicable teaching modalities.

Keywords:

Agent-Based Modeling, Iconic Representation, Gamification, Cognitive Load, Critical Thinking, Science Education.