Research On The Student Model Of Adaptive Learning Support System

Adaptive learning support system has been the focus topic in the research field of artificial intelligence in education in recent years, which is a cross areas of education, cognitive science and computer science. However, current e-learning system sends the same teaching content to all learners without concerning individual differences of learners, such as the prior knowledge, learning goal and learning style, which results in the phenomenon of cognitive overload and disorientation, and seriously affects the quality of e-learning. So many researches shift from tranditional e-learning platform to adaptive and personalized learning environment. Adaptive learning support system in essence is a kind of personalized online learning environment, which can provide adaptive learning support according to individual characteristics of learner, including personalized learning resources, learning processes and learning strategies.In this paper, we focus on the student model and adaptive teaching content organization of adaptive learning support system. Student model is the key component of adaptive learning support system, which records the learner's individual characteristics, reflecting the individual differences of learners, and provides a basis for decision making of adaptive learning support system. And adaptive teaching content is customized in accordance with the individual characteristics of learners by adaptive learning support system, which is the most important way to support learning, and is the most obvious manifestation of adaptation of adaptive learning system.In this paper, the literature, systematic approach and design-based research methods are adopted. The main works of this research as follows:Firstly, we summarize the being intelligent process of computer-aided instruction based on literature review, and analyze the strengths and weaknesses of intelligent tutoring system, and hold that adaptive learning support system is the current trend of the e-learning platform.Secondly, based on detailed analysis of the Enhancing Adaptive Hypermedia Application Model (EAHAM), we proposes the architecture of adaptive learning support system, including the media space, domain knowledge model, student model, context model, teaching strategies model and adaptive model, and adding the adaptive learning module, learning strategies modules and learning tools modules. The architecture of adaptive learning support system based on EAHAM refines the system components and the operating mechanisms, which can provide adaptive learning support in accordance with the prior knowledge, learning style and other aspects of individual differences of learners, and has good operability in system implementation.Thirdly, we set up the student model based on cognitive state and learning style, which records the individual differences of learners. Through the detailed analysis of typical student model in intelligent tutoring system, we consider that the main functions of student model in intelligent tutoring system is diagnosing and recording the student's knowledge state, especially the misconceptions in problem-solving process. This student model is confined to the learner's knowledge state, whereas it lacks of understanding other individual characteristics of learners. In order to reflect the learner's individual differences, such as the prior knowledge, learning goal, and learning style, we put forward a student model based on cognitive state and learning style. New student model mainly includes the student profiles, learning style, cognitive state, as well as learning history. Among them, learner's cognitive state and learning style is the major adaptive dimensions of EAHAM-based adaptive learning support system. In this paper, in order to describe the learning style of learner, Felder-Silverman learning style model (FSLSM) is adopted. When learner registers in adaptive learning support system, learning style is initialized through learner filling in the ILS questionnaire.Fourthly, according to the individual differences of cognitive state and learning style in student model, this paper puts forward the dynamically generation process model of adaptive teaching content, which includes both the process. Firstly, system dynamically generates teaching content sequences in accordance with the cognitive abilities of learners. Based on the hierarchical structure of domain knowledge, the task- centered teaching strategies and classification of learners' cognitive ability level, adaptive teaching content is dynamically organized. Secondly, Adaptive learning support system provides adaptive presentation strategies of teaching content in accordance with the "perception - input" dimensions of FSLSM, and provides adaptive navigation strategies in accordance with the "processing - understanding" dimensions of FSLSM. Adaptive teaching content is packaged by SCORM standards, and is adaptively annotated based on learner's cognitive states in the learning process.Fifthly, we diagnose the learner's cognitive ability based on computerized adaptive testing technology, and dynamically update student model in accordance with the test results. Based on three-parameters logistic model, the architecture of adaptive online testing system is proposed, and item bank, item selection algorithm, ability evaluation algorithm and test termination conditions are analyzed. As well as against National Excellent Courses "modern educational technology", we develop the MET-CATS prototype of computerized adaptive testing system, and analyze its evaluation process. According to the results of computerized adaptive testing, system will be divided cognitive abilities of learners into the primary level, intermediate level and advanced level.