Seeing the Forest through the Trees Using Network Analysis: Exploring Student Responses to Conceptual Physics Questions
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Seeing the Forest through the Trees Using Network Analysis: Exploring Student Responses to Conceptual Physics Questions Mihwa Park 1
# Springer Nature B.V. 2020
Abstract This study explored how scientific ideas interacted in college students’ scientific explanations using text analysis techniques and network analysis. A task was given to first-year college students asking them to answer a series of physics questions associated with a computer simulation. Students’ written responses were classified into three models—non-normative, mixed, and normative—and then subjected to text analysis using IBM SPSS Modeler with text analytics software. Using the text analysis result, a decision tree model was applied to find what ideas were significantly attributed to the different models of responses. Finally, network analysis was used to reveal and illustrate how the important attributes were used and connected with each other across the three models. The findings support the importance of teaching a small number of core ideas as the premise of the Framework for K-12 Science Education, and indicates that integrated knowledge should emphasize organizing knowledge around the core ideas. Keywords Network analysis . Text analysis . Student reasoning . Energy concept
Introduction Recent science education reforms have emphasized the enhancement of students’ reasoning from evidence and their ability to use scientific knowledge and practices in solving science problems (National Research Council 2014; Pellegrino 2013). Previous studies have found that when solving a science problem, novices tend to focus on surface features of a problem, while experts connect key scientific ideas relevant to a question and apply appropriate scientific laws or principles (Chi et al. 1982). These different approaches to solving physics problems are possibly due to the difference in experts’ and novices’ knowledge structures. Experts’ knowledge is well organized into structures (Gick 1986), while novices’ knowledge is fragmented; it is not organized by overarching theories, but rather is a repertoire of multiple quasi-independent knowledge elements (diSessa 2002). Linn (2006) stated that novices’ fragmented knowledge results from traditional instruction, which typically presents scientific ideas as isolated from one another. Thus, science instruction * Mihwa Park [email protected] 1
Department of Curriculum and Instruction, College of Education, Texas Tech University, Box 41071, Lubbock, TX 79409-1071, USA
should be designed to help novices (students) restructure and integrate knowledge elements and ideas to make appropriate connections among them (diSessa 2002; Linn et al. 2003). While conceptual change researchers in science education paid attention to moments when scientifically normative ideas replaced non-normative ideas (or naïve ideas) (Linn 2006), several empirical studies found co-existence of normative and non-normative ideas in students’ explanations of scientific phenomena (e.g., Nehm and Schonfeld 2008; Sripathi et al. 2019). For exampl
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