This paper came up with introducing four dimensions: learning mood, cognitive state, learning style and interest preference and so on for the semantic web, based on the analysis the present situation student models at home and abroad. Realized to classify learners and establish student models which used an improved similarity algorithm, and the student model applied to adaptive learning system, which not only could solve the lack semantic in adaptive learning system, and greatly improves the practicability, intelligent and personalized.
With the promotion of online education, the adaptive learning system has attracted attention due to its good curriculum recommendation function. The student model is an important interface between the adaptive learning system and the user, reflecting the individual characteristics, knowledge status, and cognitive ability of the student. The accuracy of the information in the student model directly affects the quality of the system recommendation service. The traditional student model only judges students based on the basic information and simple test scores. This paper introduces the self-adaptive item bank and adaptive item selection strategy based on the cognitive diagnosis theory that dynamically detects the students' knowledge and analyzes the state according to the answering habits and knowledge mastering status of different students. This paper analyzes and contrasts a variety of traditional cognitive diagnosis theories and proposes a mixed cognitive diagnosis question bank and a selection strategy model to provide strong support for the construction of student models.
Research on the Construction of a Student Model of an Adaptive Learning System Based on Cognitive Diagnosis Theory
