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Modeling Practices as a Function of Task Structure

To reason about complex natural systems, learners need opportunities to develop and represent their ideas about how these systems operate. In our work, we use an explicit conceptual representation – the Structure-Behavior-Function (SBF) ontology (Goel et al., 1996; HmeloSilver et al., 2007; Goel et al. 2009) – to help middle school students model and organize ideas about a one such system, the aquarium ecosystem. Our learning environment embeds SBF thinking in technology including simulations, digital modeling environments, and hypermedia (Hmelo-Silver et al., 2008). These tools allow students to formulate, test, refine, and repair their mental models of the system dynamically. In particular, we report on one aspect of this work which focuses on the second theme of the symposium, the development and study of tools to support students in working with models. In this paper, we present data on how three different teachers used the same tool to achieve their learning goals through different task structures. Further, we provide an analysis of student-generated models from each of these classrooms based on an SBF coding scheme to gain a better understanding the type of knowledge that students are representing through modeling practices. We provide evidence on how making the SBF representation scheme explicit can foster student representations that go beyond the structural representation (the “what”) of a system and include articulations that provide dynamic and procedural aspects (the “how” and “why”). Introduction: Engaging in modeling activities allows students to develop and refine scientific explanations. Such scientific practices should be included as an essential component of the science classroom experience (Duschl & Grandy, 2008). Although models are an essential part of scientific inquiry, teachers often employ models as a way to directly communicate existing knowledge (Van Driel, et al., 1999; Teagust, 2002) rather than guiding inquiry to develop deep understanding of scientific phenomena. When models are used simply as static representations, students are rarely given the opportunity to use these representations for analysis, prediction or to understand reasons that underlie dynamic processes (Carey & Smith, 1993; Van Driel & Verloop, 1999). Computer simulations provide an excellent context in which students can engage in modeling (Clement 2002). These opportunities are especially important when teaching about complex systems because they provide a way in which students can represent multiple levels of abstraction and shape ideas which represent different temporal and spatial scales simultaneously (Hmelo et al., 2001, Hmelo, Nagarajan, & Day, 2000). Understanding abstraction across different dimensions, although critical for scientific inquiry, is not easily fostered through the use of static tools, such as text books and physical models common to many science classrooms. Even when provided with such technology-based interventions, classroom enactments are largely dependent on teacher understanding of the tools being used to test ideas (e.g., Justi and Van Driel 2005) and of technology as a scaffold (Leinhardt & Steele, 2005). Although computer based modeling is largely mediated by student groups, the teacher is important in setting classroom norms (Webb et al., 2006). For example when Puntambekar, Stylianou, & Goldstein (2007) studied teacher enactment of new technology in the classroom, they found significant differences in learning gains that can be related to differing teacher practices in areas beyond the use of the tool. Further, Gray et al. (2008) found that two teachers although given very similar tools, enacted two very different classrooms based on different views of computers, simulations, and classroom inquiry.