In this study we articulate a multi-level scientific Modeling Practices Framework derived from expert studies on model based teaching strategies in classrooms and examine its usefulness in an actual classroom context. In addition, we develop vocabulary and diagrams to describe a multi level model based teaching processes. We are particularly interested in examining: (1) Is there a pattern of model construction processes that occurs over a large time scale of 3-6 lessons? (2) Is there a pattern of model construction processes that occurs over a medium sized time scale of 5-20 min cycles within lessons? (3) If present, how are these patterns connected? And (4) Do these patterns suggest a set of model development strategies for teachers? We conducted a detailed case study of a three-lesson cluster through which middle school students build an explanatory model about how the glucose goes from the small intestine to the blood and to the cells. The main findings of the study are included in a Classroom Dialogue Diagram that describes six Major Modeling Modes occurring at a large scale over 3 lessons and several smaller Model Construction Processes occurring at a medium sized time scale. The diagram provides a more detailed description of the modeling processes than those included in the NGSS. In addition, we found that the medium sized Model Construction processes are nested within the larger Major Modeling Modes, and act as subprocesses for them. Furthermore, we hypothesize that the six Major Modeling Modes could be understood as a “repeated Mode sequence” to guide instruction in the classroom. We hypothesize that this flexible modeling sequence can be used to teach different topics and subtopics, as a way of aiming for deeper conceptual knowledge along with learning modeling practices.
Nunez-Oviedo, M. & Clement, John J. (2017). Large Scale Scientific Modeling Practices that can Influence Science Instruction at the Unit and Lesson Levels. Paper presented at the National Association for Research in Science Teaching (NARST), San Antonio, TX.