Improving knowledge tracing model by integrating problem difficulty

• المؤلفون الرئيسيون: MINN, Sein, ZHU, Feida, DESMARAIS, Michel C.
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2018
• الموضوعات: deep learning; complex skill composition; problem difficulty; knowledge tracing; Student model; robust learning; Artificial Intelligence and Robotics; Computer Sciences; Educational Methods
• الوصول للمادة أونلاين: https://ink.library.smu.edu.sg/sis_research/4327; https://ink.library.smu.edu.sg/context/sis_research/article/5330/viewcontent/ImprovingKnowledgeTracingModel_pv.pdf

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.