Cultivation of College English Network Autonomous Learning Ability Based on the Multisource Information Fusion Algorithm

• Published in: Mobile information systems Vol. 2022; pp. 1 - 12
• Main Author: Liu, Wenjun
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 23.07.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Animal cognition; College students; Colleges & universities; Cultivation; Data integration; Education; Foreign language learning; Foreign languages; Information technology; Language; Learning strategies; Lifelong learning; Machine learning; Mental health; Monitoring; Motivation; Online instruction; Psychiatric-mental health nursing; R&D; Research & development; Research methodology; Second language instruction; Self instruction; Students; Teaching methods; Unmanned aerial vehicles
• ISSN: 1574-017X; 1875-905X; 1875-905X
• DOI: 10.1155/2022/8192410

With the rapid development of science and technology, the new century has entered an era of ever-changing information. Lifelong learning and lifelong education have become the dominance of a new round of educational concepts. Therefore, it is urgent to cultivate learners’ autonomous learning ability. Self-directed learning has been a major focus of foreign language education in recent years. This research mainly explores the cultivation of college English online autonomous learning ability based on the multisource information fusion algorithm. This research will use a multisource information fusion algorithm to collect factors such as motivation, learning time, and other factors of students’ English learning from multiple dimensions and then evaluate the college English online self-learning ability. The questionnaire on the self-learning ability of college students in the network environment includes five dimensions: understanding of teachers’ teaching objectives and requirements, formulation and planning of learning objectives, effective use of learning strategies, monitoring of the use of learning strategies, and monitoring and evaluation of the learning process. The results of the study found that the highest score of the control class was 96 and the highest score of the experimental class was 86. The average score of the control class was 73.6 and the average score of the experimental class was 78.4, indicating that the average score of the experimental class was 4.8 points higher than that of the control class, and the standard deviations of the two classes were, respectively, 8.05 and 6.51. High-level students are better at using the network for effective learning than low-level students. High-level students are better at choosing learning strategies that suit them than lower level students. The value of sig (2-tailed) in the t-test is 0.004, which is less than 0.05. Therefore, we conclude that there is a significant difference in the posttest results of the two classes, showing a significant difference. This research will help to promote the cultivation of college English online self-learning ability.