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Using Networks to Visualize and Analyze Process Data for Educational Assessment
Author(s) -
Zhu Mengxiao,
Shu Zhan,
Davier Alina A.
Publication year - 2016
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12107
Subject(s) - rubric , computer science , process (computing) , visualization , data science , action (physics) , data visualization , educational assessment , data mining , mathematics education , physics , quantum mechanics , operating system , mathematics
New technology enables interactive and adaptive scenario‐based tasks (SBTs) to be adopted in educational measurement. At the same time, it is a challenging problem to build appropriate psychometric models to analyze data collected from these tasks, due to the complexity of the data. This study focuses on process data collected from SBTs. We explore the potential of using concepts and methods from social network analysis to represent and analyze process data. Empirical data were collected from the assessment of Technology and Engineering Literacy, conducted as part of the National Assessment of Educational Progress. For the activity sequences in the process data, we created a transition network using weighted directed networks, with nodes representing actions and directed links connecting two actions only if the first action is followed by the second action in the sequence. This study shows how visualization of the transition networks represents process data and provides insights for item design. This study also explores how network measures are related to existing scoring rubrics and how detailed network measures can be used to make intergroup comparisons.