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
Citation
MINN, Sein; ZHU, Feida; and DESMARAIS, Michel C..
Improving knowledge tracing model by integrating problem difficulty. (2018). 2018 IEEE International Conference on Data Mining Workshops 18th ICDMW: Singapore, November 17-20: Proceedings. 1505-1506.
Available at: https://ink.library.smu.edu.sg/sis_research/4327
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICDMW.2018.00220