Application and practice of personalized education based on big data analysis in Java object-oriented program design

This paper delves into the application and practice of personalized education based on big data analysis in the teaching of Java object-oriented programming. By constructing a big data analysis model, we have achieved precise collection and processing of student learning data, thereby tailoring pers...

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Vydáno v:MATEC web of conferences Ročník 395; s. 1041
Hlavní autoři: Xing, Xuexia, Yanan, Zhai
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Les Ulis EDP Sciences 2024
Témata:
Big Data
big data analysis
Customization
Data analysis
Education
java object-oriented programming
Learning
Object oriented programming
personalized education
Professional development
Students
ISSN:2261-236X, 2274-7214, 2261-236X
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Shrnutí:This paper delves into the application and practice of personalized education based on big data analysis in the teaching of Java object-oriented programming. By constructing a big data analysis model, we have achieved precise collection and processing of student learning data, thereby tailoring personalized learning paths and resource recommendations for each student. The aim of this paper is to analyze the implementation effects of this innovative educational model in the teaching of Java object-oriented programming, with the hope of providing valuable insights for improving teaching quality and student learning outcomes. Furthermore, we also explore the advantages of applying Java object-oriented programming in personalized education. The object-oriented nature of Java makes program design more aligned with human thinking patterns, aiding in the development of students' logical thinking and innovative capabilities. Additionally, the widespread use of the Java language also offers expansive opportunities for students' future career development.
Bibliografie:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/202439501041