Design of ADCF-based Online Learning Resource Recommendation System for Preschool Education
Jiaopeng Nan, Bin Li · 2023
Online learning has emerged as a brand-new method of knowledge transfer as a result of the Internet and the field of education coming together due to the rapid development of information technology. The proliferation of online learning tools, particularly in the area of preschool education, gives pupils more learning opportunities but also creates issues with selection and learning effectiveness. The goal of this study is to develop an Attention-based Deep Collaborative Filtering (ADCF) preschool online learning resource recommendation system by combining the attention mechanism collaborative filtering algorithm and deep learning algorithm. This system will assist students in selecting the most appropriate online learning resources from a large pool of online learning course resources and increase learning effectiveness. The results show that the average scores of the experimental class’s curriculum paper, early childhood teaching activity design and kindergarten game creation were 94.58, 88.15 and 92.43, respectively, which were 6.10%, 7.63% and 1.97% higher compared with the control class’s average scores of 89.14, 81.90 and 90.64, respectively. This study has significant practical implications for enhancing the recommendation of online learning resources and raising the standard of preschool education, as can be seen in the conclusion.