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Exploration of the Precise Teaching Path of Theory Courses in Colleges and Universities Empowered by Big Data

Year: 2024
Type: article
Abstract: With the development of big data technology, the teaching mode of theoretical courses in colleges and universities needs to be changed urgently. This study first systematically explains the relevant concepts of big data and precision teaching, and clarifies the basic framework and value of data analysis and application in the field of education. Subsequently, through an in-depth analysis of the teaching status quo of the existing theory courses in universities, it points out the problems that exist in the current teaching process, including the uneven distribution of teaching resources and the unrefined assessment of learning effects. Based on the above findings, this paper proposes a diversified strategy for big data-enabled accurate teaching of theory courses in colleges and universities, focusing on the collection and analysis methods of learning behaviour data and learning effect assessment to optimize the teaching process from a data-driven perspective. Through case studies, specific strategies and effects in practical application are demonstrated, emphasising how the precise setting of teaching objectives and rational allocation of teaching resources can enhance the quality of courses and students' learning experience. Practical suggestions for improvement are made to address the challenges encountered in the implementation process, such as user privacy and data security, and insufficient teacher skill requirements. The study shows that through the systematic application of big data, university theory courses can achieve more precise teaching objectives and personalised learning experiences, thus promoting educational equity and teaching quality.
DOI: 10.62517/jhve.202416514
Authors: Wang Linlin, Guo Siyuan
Language: English
Open Access status: hybrid
OpenAlex ID: W4409884692
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