Influencing Factors of Students’ Acceptance of Blended Learning Based on Cognitive Neural Network

Abstract

In order to study the influencing factors of students’ acceptance of blended learning, the structural equation model is used to establish a model of students’ acceptance of blended learning, BP neural network is applied to analyze the effect strength of each factor on the acceptance of blended learning, and empirical study is conducted to verify the impact of perceived ease of use, perceived usefulness, learning atmosphere, and interactive behavior on the students’ acceptance of blended learning. As the research results show, perceived ease of use and perceived usefulness are important factors affecting the acceptance of blended learning; Factors such as learning atmosphere and interactive behavior also affect the acceptance of blended learning, as the former can effectively enhance learning interest and stimulate learning enthusiasm, while the latter determines the frequency and intensity of blended learning exchanges and is an important influence factor for deepened learning; When learning background is introduced into the study of influencing factors, it is found that learners' learning background plays an important role in learning process and learning effect, and is also a key factor among many influencing factors; Learning background has a direct impact on the quality of learning. It has a clear role in adjusting perceived ease of use and learning atmosphere, but it does not have an obvious regulatory effect on perceived usefulness and interactive behavior.