Research on Adaptive Learning System Based on Three Core Modules

• Published in: IEEE International Symposium on IT in Medicine and Education pp. 447 - 452
• Main Authors: Zhao, Lili, Wang, Hong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2019
• Subjects: Adaptation models; Adaptive learning; adaptive learning system; adaptive recommendation; Adaptive systems; cognitive model; Knowledge engineering; knowledge network construction; Learning systems; Navigation; personalized learning
• ISSN: 2474-3828
• DOI: 10.1109/ITME.2019.00106

Adaptive learning system is a highly intelligent learning resource service model. It can provide learners with personalized learning paths and learning resources, and improve learning by combining specific behavioral characteristics such as user learning habits, learning methods and knowledge capacity, make each student can have his own "exclusive teacher."This paper focuses on the three core modules of the adaptive learning system-the knowledge network model, the cognitive level model and the adaptive recommendation model. Firstly, based on Ebbinghaus memory theory, students' memory rules are explored; knowledge network model is constructed by knowledge graph to build knowledge trees; dual-subject recommendation mechanism is adopted in adaptive recommendation models to integrate users and systems. Secondly, this paper designs a knowledge network construction method suitable for describing and demonstrating the characteristics of university courses, that is, using the knowledge tree to visualize knowledge points. Finally, this paper designs a representation method that can effectively distinguish the knowledge of different students. When using clustering and lag sequential analysis to determine the cognitive level of students, the "red and green traffic lights" form is used to grasp the knowledge points. This is also an element labeling method to reflect the progress of learning more vividly. The research results show that the resulting three-core module has better performance and higher adaptability. The conclusions of this study help to establish a relatively complete adaptive learning system to promote students' self-learning.