Construction and Empirical Research of the Big Data-Based Precision Teaching Paradigm

Xinli Wu, Jie Chang, Fei Lian, Liheng Jiang, Juntong Liu, Robail Yasrab

OAI: oai:igi-global.com:313411 • DOI: 10.4018/IJICTE.313411

The rapid development of big data technology has attracted a variety of sectors, including tertiary education. The purpose of this paper is to construct a precision teaching mode based on big data technology in order to improve teaching quality and further promote education and teaching reform. The proposed mode, based on the theory of precision teaching in colleges and universities as well as the intrinsic properties of big data teaching activities, describes five procedures for analyzing learning situations, determining teaching goals, preparing teachers, and evaluating teachers. When the big data-based precision teaching mode is applied to the “Python Language Programming” course, the results show that students are more satisfied with the design of the teaching and more efficient in learning. It is believed that this mode will significantly improve students' academic performance and their ability to work independently and collaboratively as a result of more frequently online and offline interactions between teachers and students.