Knowledge Tracing (KT) is a crucial task in the field of online education, since it aims to predict students' performance on exercises based their learning history. One typical solution for knowledge tracing combine classic models educational psychology, such as Item Response Theory (IRT) and Cognitive Diagnosis (CD), with Deep Neural Networks (DNN) technologies. In this solution, student relat...

Abstract The knowledge tracing (KT) model is an effective means to realize the personalization of online education using artificial intelligence methods. It can accurately evaluate learning state students and conduct personalized instruction according characteristics different students. However, current models still have problems inaccurate prediction results poor features utilization. study ap...

For an e cient linguistic analysis of spoken queries a lot of domain speci c knowledge is needed and usually has to be entered manually into the knowledge base of each domain. This makes the adaption of dialogue systems which base on explicit knowledge representation to new domains a very costly procedure. We use a frequency based statistical method combined with general hidden markov models in...

Learning of complex material, such as concepts encountered in science classrooms, can occur under at least three different conditions of prior knowledge. First, a student may have no prior knowledge of the to-be-learned concepts, although they may have some related knowledge. In this case, prior knowledge is missing, and learning consists of adding new knowledge. Second, a student may have some...

In this paper, we discuss the development and use of a Knowledge Dictionary, a tool to facilitate the documentation and maintenance of rule based expert systems. The Knowledge Dictionary may be used to record heuristics and their component parts, facts and rule actions,utilizing the relational data model to store the heuristics in a data form rather than executable code form. Thus knowledge bas...