Bifocal Modeling: Summative Results of Studies Combining Real-World Data and Computational Models

• In Event: Thinking Across Representations: Designing to Support Investigations That Leverage Physical and Computational Modes of Inquiry

Abstract

Objectives and theoretical framework. Philosophers of science have been calling into question the ‘‘text bias’’—a tendency to consider literary production as the ultimate product of science (see Baird, 2006; Latour & Woolgar, 1979). The idea that scientific instruments are more than disposable means by which scientific truths are brought into being has garnered interest in recent years, and researchers have proposed the reexamination of the canonical scientific method in education (Duschl & Grandy, 2008; Nersessian, 2005; Radder, 2003). This presentation will discuss Bifocal Modeling (Author, 2007; Author, 2010; 2012), the first established K-12 framework that embraces these complex relationships between instruments and theory, real and ideal systems, tangible and virtual apparatus (see Figure 1 for framework and Figure 2 for examples).
Bifocal Modeling is an inquiry-based approach for science learning that challenges students to design, compare, and examine the relationships between lab experiments and computer models connected in real-time (this dual focus motivates the “bifocal” designation.) In a typical Bifocal Modeling activity, students explore a phenomenon by designing and interacting with a physical experiment (often equipped with electronic sensors) and a computer model of the same phenomenon, comparing them (often) in real-time. The aim is to emphasize the relationship between the theorized behavior represented in the computer model, and the real-world data stream coming from the experiment.

Methods and data. We will provide summative results from several studies conducted over 7 years with middle- and high-school students, exemplifying the main findings from this multi-year effort (Author, 2016; Author, 2018). One cluster of studies, which are qualitative in nature and based on semi-clinical interviews, artifact analysis and field notes, will document the cognitive and epistemological outcomes of model comparison. The second cluster, quantitative in nature, will document controlled studies done in several diverse classrooms with hundreds of students, in which we varied the presence and type of implementation of Bifocal Modeling.

Results and Significance. Findings from the first cluster suggested that as students compared their virtual models with physical experiments, they encountered “discrepant events” that contradicted their existing conceptions and elicited a state of cognitive disequilibrium. This experience of conflict encouraged students to seek more accurate explanations and reexamine current conceptions, as well as pay more attention to issues of scientific precision, intrinsic measurement error, and to the differences between real and ideal systems. Data also suggested that students were more confident in their abilities as scientists in the classroom context when they came to understand that the work of scientists is often imprecise and that scientific laws are only approximations of the real phenomena. The second cluster of studies, done in middle- and high-school classrooms within typical K-12 constraints, showed that students who participated in Bifocal Modeling activities demonstrate a statistically significant improvement of their understanding of the target content and their meta­modeling knowledge, with stronger effects when the treatment groups designed their own models instead of just interaction with pre-made models.

Authors

• Paulo Blikstein, Teachers College, Columbia University

• Tamar Fuhrmann, Columbia University