The dynamics of variability in introductory physics students' thinking: Examples from kinematics.

摘要

Physics education research has long emphasized the need for physics instruction to address students' existing intuitions about the physical world as an integral part of learning physics. Researchers, however, have not reached a consensus-view concerning the nature of this intuitive knowledge or the specific role that it does (or might) play in physics learning. While many early characterizations of student misconceptions cast students' intuitive thinking as largely static, unitary in structure, and counter-productive for the purpose of learning correct physics, much of contemporary research supports a conceptualization of intuitive thought as dynamic, manifold in structure, and generative in the development of expertise. This dissertation contributes to ongoing inquiry into the nature of students' intuitive thought and its role in learning physics through the pursuit of dynamic systems characterizations of student reasoning, with a particular focus on how students settle into and shift among multiple patterns of reasoning about motion.;In one thread of this research, simple experimental designs are used to demonstrate how individual students can be predictably biased toward and away from different ways of thinking about the same physical situation when specific parameters of questions posed to students are varied. I qualitatively model students' thinking in terms of the activations and interactions among fine-grained intuitive knowledge and static features of the context. In a second thread of this research, case studies of more dynamic shifts in students' conceptual reasoning are developed from videos of student discussions during collaborative classroom activities. These show multiple local stabilities of students' thinking as well, with evidence of group-level dynamics shifting on the time scale of minutes.;This work contributes to existing research paradigms that aim to characterize student thinking in physics education in two important ways: (1) through the use of methods that allow for forms of empirical accountability that connect descriptive models of student thinking to experimental data, and (2) through the theoretical development of explanatory mechanisms that account for patterns in students' reasoning at multiple levels of analysis.