Research on personalized recommendation method of preschool e-learning resources

Abstract

E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8.

Keywords

• e-learning
• online learning resources
• personalized recommendation
• collaborative filtering algorithm
• learner model
• 97m80