Paper Info
Title
Exploring networks of problem-solving interactions
Abstract
Intelligent tutoring systems and other computer-aided learning environments produce large amounts of transactional data on student problem-solving behavior, in previous work we modeled the student-tutor interaction data as a complex network, and successfully generated automated next-step hints as well as visualizations for educators. In this work we discuss the types of tutoring environments that are best modeled by interaction networks, and how the empirical observations of problem-solving result in common network features. We find that interaction networks exhibit the properties of scale-free networks such as vertex degree distributions that follow power law. We compare data from two versions of a propositional logic tutor, as well as two different representations of data from an educational game on programming. We find that statistics such as degree assortativity and the scale-free metric allow comparison of the network structures across domains, and provide insight into student problem solving behavior.
Year
DOI
Venue
2015
10.1145/2723576.2723630
LAK
Keywords
Field
DocType
group concept mapping
Assortativity,TUTOR,Group concept mapping,Empirical evidence,Computer science,Propositional calculus,Theoretical computer science,Degree (graph theory),Complex network,Transaction data
Conference
Citations 
PageRank 
References 
18
1.22
17
Authors
4
Name
Order
Citations
PageRank
Michael Eagle118824.34
Andrew Hicks27210.03
Barry W. Peddycord III3342.35
Tiffany Barnes429866.88