Research on Digital Teaching Resources Development and Personalized Teaching in Sensor Courses

Abstract

In the era of rapid development of computer technology and education technology, personalized teaching can provide learners with learning content that meets their needs and achieves the teaching purpose of “tailor-made teaching”. In this paper, the MOB method is used to reorganize and describe digital teaching resources after the coding process so as to achieve the development and management of digital teaching resources in sensor courses. Then, the hybrid differential evolution algorithm is used to cluster similar learning resources and learners in the learning process, and the recommendation of personalized digital teaching resources in sensor courses for students is realized through the operations of mutation and selection. Based on this, a personalized teaching model is constructed for teaching sensor courses. After testing, the personalized teaching model was found to be able to accurately analyze the learners’ knowledge of each knowledge point, and its accuracy is much better than the benchmark model. The empirical analysis shows that after applying the personalized teaching model to assist teaching, the average score of students’ sensor course test scores increased from 65.26 to 90.22 (P<0.01), and their learning attitudes were also significantly improved. This paper can provide teachers and administrators with some new ideas about optimizing classroom teaching practices and provide references for implementing and promoting classroom teaching reform in schools.

Keywords

• mob method
• cognitive diagnosis
• hybrid differential evolutionary algorithm
• sensor course
• personalized recommendation
• 68p30