Abstract
The viability of using complexity science in physics education research (PER) is exemplified by (1) situating central tenets of student persistence research in complexity science and (2) drawing on the methods that become available from this to illustrate analyzing the structural aspects of students’ networked interactions as an important dynamic in student persistence. By drawing on the most cited characterizations of student persistence, we theorize that university environments are made up of social and academic systems, which PER work on student persistence has largely ignored. These systems are interpreted as being constituted from rules of interaction that affect the structural aspects of students’ social and academic network interactions from a complexity science perspective. To illustrate this empirically, an exploration of the nature of the social and academic networks of university-level physics students is undertaken. This is done by combining complexity science with social network analysis to characterize structural similarities and differences of the social and academic networks of students in two courses. It is posited that framing a social network analysis within a complexity science perspective offers a new and powerful applicability across a broad range of PER topics.Received 12 March 2014DOI:https://doi.org/10.1103/PhysRevSTPER.10.020122This article is available under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical Society
Highlights
In his 2013 Oersted lecture, Redish pointed out that, “despite the clear value of theoretical frameworks in scientific research, DBER [Discipline Based Education Research] scientists are often reluctant to situate their work within a theoretical framework” (p. 3) and that “part of the challenge in building educational theory” is that “human behavior is extremely complex.” He further argued that, “Just as we have done in many areas of science, we create a theoretical framework that allows us to build descriptive models and that can evolve and change as we learn more” ([1], p. 2)
To create such theoretical framing for physics education research (PER) it is inevitable that the physics education community will draw from other scientific areas such as education, physics, sociology, psychology, neuroscience, biology, and mathematics
We provide a theoretical framework through which it is possible to interpret the collection of tools provided by social network analysis
Summary
In his 2013 Oersted lecture, Redish pointed out that, “despite the clear value of theoretical frameworks in scientific research, DBER [Discipline Based Education Research] scientists are often reluctant to situate their work within a theoretical framework” (p. 3) and that “part of the challenge in building educational theory” is that “human behavior is extremely complex.” He further argued that, “Just as we have done in many areas of science, we create a theoretical framework that allows us to build descriptive models and that can evolve and change as we learn more” ([1], p. 2). 3) and that “part of the challenge in building educational theory” is that “human behavior is extremely complex.”. He further argued that, “Just as we have done in many areas of science, we create a theoretical framework that allows us to build descriptive models and that can evolve and change as we learn more” “Just as we have done in many areas of science, we create a theoretical framework that allows us to build descriptive models and that can evolve and change as we learn more” Modeling of complex systems—or the application of complexity science or complexity theory. Our aim is to illustrate an application of it for a long-known thorny issue—student persistence
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