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Autoscoring Essays Based on Complex Networks
Author(s) -
Ke Xiaohua,
Zeng Yongqiang,
Luo Haijiao
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.12127
Subject(s) - clustering coefficient , complex network , computer science , cluster analysis , artificial intelligence , representation (politics) , adjacency list , feature (linguistics) , word (group theory) , natural language processing , complex system , series (stratigraphy) , linguistics , algorithm , world wide web , paleontology , philosophy , politics , political science , law , biology
This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale‐free networks (e.g., a word adjacency model) from which typical network features, such as the in‐/out‐degrees, clustering coefficient (CC), and dynamic networks, were obtained. The CDES integrates the classical concepts of network feature representation and essay score series variation. Several experiments indicated that the network measures different essay qualities and can be clearly demonstrated to develop complex networks for autoscoring tasks. The average agreement of the CDES and human rater scores was 86.5%, and the average Pearson correlation was .77. The results indicate that the CDES produced functional complex systems and autoscored Chinese essays in a method consistent with human raters. Our research suggests potential applications in other areas of educational assessment.