Lowering the Learning Threshold: Multi-Agent-Based Models and Learning Electricity

Electromagnetism, in particular, electricity, is a notoriously hard topic for students at all age levels (Belcher & Olbert, 2003; Cohen, Eylon, & Ganiel, 1983; Eylon & Ganiel, 1990; White, Frederiksen, & Spoehr, 1993; etc.). The difficulty in understanding basic phenomena such as electric current, electric potential difference (or voltage), electric resistance is often displayed in the novices’ explanations involving behavior of simple electrical circuits. Furthermore, misconceptions that stem from these difficulties have been regarded by several researchers as resistant to change due to instruction (Cohen et al., 1983; Hartel, 1982) and indicative of a discontinuity between expert and novice knowledge systems (Chi, Slotta, & Leauw, 1994; Reiner, Slotta, Chi, & Resnick, 2000). Our prior work has shown that the problems faced by novice learners in the domain of electricity can also be understood in terms of the difficulties faced by novices in understanding behaviors of a complex system, i.e., systems in which phenomena at one level emerge from interactions between objects at another level (Sengupta &Wilensky, 2009;Wilensky &Resnick, 1999). For example, a traffic jam can be considered an aggregate level phenomenon which arises form simple interactions (such as moving forward, braking) between many individual level agents (i.e., cars). In this chapter, we build on this research and demonstrate how a suite of emergent, multi-agent-based computational models (NIELS: NetLogo Investigations in Electromagnetism; Sengupta & Wilensky, 2008a, 2008b) can be designed to represent electricity in linear resistive systems in a manner that is intuitive and easily understandable by a wide range of physics novices: from 5th-grade to 12th-grade students. At the heart of our thesis is the idea that such an emergent perspective enables novices to develop an understanding of electrical conduction by bootstrapping, rather than discarding their intuitive knowledge (Sengupta &Wilensky, 2009; under