Investigating processes and products of secondary science students using dynamic modeling software.

Abstract

With the development of learner-supporting software and the availability of powerful desktop computers in classrooms, dynamic modeling is becoming accessible to secondary science students. Dynamic modeling should engage learners in analyzing phenomena into parts, reasoning about relationships between them, synthesizing relationships into a model, and testing a model's fit with reality. Cognitive strategies fostered by dynamic modeling environments should allow students to go beyond fact retention to making connections between ideas and concepts they have learned and to developing deeper understandings of scientific phenomena. This dissertation qualitatively explores, in three parts, research questions related to processes and products of students creating dynamic models of stream ecosystems using learner-centered modeling software called "Model-It." The first part examines quality and characteristics of students' Cognitive Strategies for Modeling (analyzing, relational reasoning, synthesizing, and testing/debugging), or CSMs, and General Conceptual Strategies for Modeling (planning, explaining, searching, and questioning), or GCSMs. Eight case studies of pairs of secondary students demonstrated that students' CSMs and GCSMs varied in quality, but almost all groups were able to engage in some level of thoughtful CSMs, supported by the software and by instruction. The second part is an analysis of students' models as artifacts of understanding in terms of structure, scientific content, behavior, and craft. Examination of fifty models showed that students were able to construct dynamic models that were generally coherent, accurate, and sensibly behaved, relative to students' level of experience with science and with dynamic modeling. The third part addresses the question of whether students' modeling processes were related to what they produced, that is, whether thoughtful, effective strategies led to conceptually rich products. Results indicate that students who engaged in the full range of analyzing, relational reasoning, synthesizing, and testing/debugging created models that were coherent, accurate, and exhibited reasonable behavior with fairly high fidelity to the real world. This dissertation shows that dynamic modeling is a viable, promising classroom activity fostering engagement in Cognitive Strategies for Modeling. However, its results also indicate that more investigation is needed into instructional and software support for better quality modeling processes of causal reasoning, testing and debugging, and General Conceptual Strategies for Modeling.