Research on Moral Education Function of Music Art in College Students Based on Bayesian Learning Algorithm

• Published in: Security and communication networks Vol. 2022; pp. 1 - 8
• Main Authors: Lu, Meng, Pengcheng, Du, Yanfeng, Song
• Format: Journal Article
• Language: English
• Published: London Hindawi 17.03.2022; John Wiley & Sons, Inc
• Subjects: Aesthetics; Algorithms; Bayesian analysis; Big Data; College students; Colleges & universities; Combinatorial analysis; Data analysis; Data mining; Decision making; Education; Educational technology; Emotions; Fault diagnosis; Ideology; Image processing; Knowledge; Knowledge representation; Learning; Machine learning; Moral education; Morality; Music education; Object recognition; Optimization; Pattern recognition; Reasoning; Search methods; Students; Subject specialists; Teaching
• ISSN: 1939-0114; 1939-0122
• DOI: 10.1155/2022/1809364

While paying attention to students’ acquisition of knowledge, we should also pay attention to students’ ideological and moral education. Bayesian network is a probabilistic graphical model that was developed in the 1980s. Therefore, it is urgent to establish the correct outlook on life and values of the motherland in the future. Music is one of the main categories of aesthetic education, which not only plays a role in cultivating students’ talents and skills, but also has the function of moral education, so it has its special teaching position. It provides the means of knowledge representation, reasoning, and learning in uncertain environment. Based on Bayesian learning algorithm, this paper studies the moral education function of music art in college students. Bayesian network can effectively carry out multivariate joint prediction, causal reasoning, expression of uncertain knowledge, pattern recognition, image processing, and causal data mining. Among them, the method based on score search regards structural learning as a combinatorial optimization problem, and score function and search method are two important factors that affect the learning effect.