ERIC Number: EJ1367862
Record Type: Journal
Publication Date: 2022
Pages: 14
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1550-1876
EISSN: EISSN-1550-1337
Construction and Empirical Research of the Big Data-Based Precision Teaching Paradigm
Wu, Xinli; Chang, Jie; Lian, Fei; Jiang, Liheng; Liu, Juntong; Yasrab, Robail
International Journal of Information and Communication Technology Education, v18 n2 Article 11 2022
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.
Descriptors: Precision Teaching, Learning Analytics, Teacher Evaluation, Programming Languages, Educational Quality, Educational Change, Data, Instructional Design, Academic Achievement, Blended Learning
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
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