Value Assessment of Online Educational Resources in the Context of Blended Learning

Abstract

In the context of promoting informatization education and reforming blended learning, how to accurately assess the value of online educational resources has become an important research topic. However, most existing assessment methods rely on direct data statistics and experience-based judgment, lacking scientific prediction models and comprehensive consideration of learning cycles. To solve this problem, this study constructed a hit prediction model of online educational resources based on the fusion of extreme learning machine and grey Verhulst model, and a hit-based value assessment model of the resources considering the learning cycles of students. The research results showed that the proposed model not only predicted and assessed the value of online educational resources more accurately, but also provided theoretical support and decision-making reference for the implementation of blended learning reform, thereby promoting the effective utilization of online educational resources.