Abstract
Clark, Sengupta, Brady, Martinez-Garza, and Killingsworth (2015) and Sengupta and Clark (submitted) propose disciplinarily-integrated games as a generalizable template for supporting students in interpreting, manipulating, and translating across phenomenological and formal representations in support of a Science as Practice perspective (Pickering, 1995; Lehrer & Schauble, 2006). To explore the generalizability of disciplinarily-integrated games, this chapter proposes other hypothetical examples of disciplinarily-integrated games in physics, biology, chemistry, and the social sciences. We explore disciplinarily-integrated games in three categories, beginning with the category involving the nearest and simplest transfer of the template and extending to the category involving the furthest and most complex transfer: (1) time-series analyses with Cartesian formal representations, (2) constraint-system analyses with Cartesian formal representations, and (3) other model types and non-Cartesian formal representations. We close with the discussion of the implications of this generalizability.
TopIntroduction
Clark, Sengupta, Brady, Martinez-Garza, and Killingsworth (2015) outline an approach for leveraging digital games as a medium to support the development of scientific modeling in K-12 classrooms based on the Science as Practice perspective (Pickering, 1995; Lehrer & Schauble, 2006). Clark et al. name this approach disciplinary integration and outline its development though a program of iterative research on student learning. Sengupta and Clark (submitted) extend the theoretical framing of disciplinarily-integrated games in terms of materiality within the classroom and the iterative design of multiple complementary symbolic inscriptional systems.
Clark et al. (2015) and Sengupta and Clark (submitted) propose that disciplinarily-integrated games represent a highly generalizable genre. To explore this claim of generalizability, the current chapter proposes other hypothetical examples of disciplinarily-integrated games (which we will refer to as DIGs for brevity) in physics, biology, chemistry, and the social sciences. We explore DIGs in three categories, beginning with model types and modeling strategies involving the nearest and simplest transfer of the DIG template and extending to those involving the furthest and most complex transfer:
- 1.
Time-series analyses with Cartesian formal representations,
- 2.
Constraint-system analyses with Cartesian formal representations, and
- 3.
Other model types and non-Cartesian formal representations. We close with the discussion of the implications of this generalizability.
TopBackground
The Science as Practice (or SaP) perspective (Pickering, 1995; Lehrer & Schauble, 2006a, 2006b; Duschl et al., 2007) argues that the development of scientific concepts is deeply intertwined with the development of epistemic and representational practices (e.g., modeling). Models are inscriptions and fictive representations of real things (e.g., planes, cars, or buildings) or systems (e.g., atomic structure, weather patterns, traffic flow, ecosystems, or social systems) that are simpler than the real objects and systems they represent, but preferentially highlight certain properties of the referent (Rapp & Sengupta, 2012). Modeling is generally recognized as the core disciplinary practice in science, and involves the iterative generation and refinement of inscriptions, which in turn serve as, or provide mechanistic explanations of a referent phenomenon (Giere, 1988; Nercessian, 2002; Lehrer & Schauble, 2002).
Key Terms in this Chapter
Disciplinary Integration: An approach for leveraging digital games as a medium to support the development of scientific modeling in K-12 classrooms based on the Science as Practice perspective.
Modeling Strategies (Epistemic Games): The sets of rules and strategies for creating, manipulating, and refining specific model types.
Situation Action Models: Situation-action models specify a set of if/then rules that specify what actions an agent will take in what situations. As situations change, either because of previous actions or because of changes in agent’s environment, the rules then specify the next actions (or inaction).
System Dynamics Model: In a system-dynamics model, variables are linked together by positive or negative links. Variables can be increased or decreased, which in turn changes other variables in the system though the links. These models can be qualitative or quantitative. Various lag, homeostatic, or feedback functions can be built into the models.
Cartesian Representation: Graphically representing relationships between multiple variables in a coordinate system where the variables specify signed distance perpendicular to an axis for each variable. Most commonly two dimensional relationships represented along an X and Y axis, but greater dimensionality is also feasible.
Structural-Analysis Models: Model types that include compare and contrast, cost-benefit analysis, primitive elements analysis, tables or cross-product analysis, tree structures or hierarchical analysis, and axiom systems.
Model Types (Epistemic Forms): Target structures guiding scientific inquiry for scientists.