Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining - Chaohua Fang; Qiuyun Lu (Complexity)
With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.