Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2018

Abstract

Intelligent Tutoring Systems (ITS) are designed for providing personalized instructions to students with the needs of their skills. Assessment of student knowledge acquisition dynamically is nontrivial during her learning process with ITS. Knowledge tracing, a popular student modeling technique for student knowledge assessment in adaptive tutoring, which is used for tracing student's knowledge state and detecting student's knowledge acquisition by using decomposed individual skill or problems with a single skill per problem. Unfortunately, recent KT models fail to deal with practices of complex skill composition and variety of concepts included in a problem simultaneously. Our goal is to investigate a student model that compatible for problems with multiple skills and various concept.

Keywords

deep learning, complex skill composition, problem difficulty, knowledge tracing, Student model, robust learning

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Educational Methods

Research Areas

Data Science and Engineering

Publication

2018 IEEE International Conference on Data Mining Workshops 18th ICDMW: Singapore, November 17-20: Proceedings

First Page

1505

Last Page

1506

ISBN

9781538692882

Identifier

10.1109/ICDMW.2018.00220

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1109/ICDMW.2018.00220

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