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Research on Precision Teaching in College English via Big Data-Driven Learning Diagnosis Systems

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Publicado en:Journal of Cases on Information Technology vol. 27, no. 1 (2025), p. 1-24
Autor principal: Chen, Jianwei
Otros Autores: Yan, Jinyan
Publicado:
IGI Global
Materias:
Students
Teaching methods
Curricula
Higher education
Medical diagnosis
Intervention
Classroom communication
Education policy
Diagnosis
Teaching
Educational technology
Homework
Learning management systems
Data quality
Distance learning
Teachers
Colleges & universities
Big Data
Data integrity
Learning
Multidimensional data
Mental health
Educational systems
Education reform
Language
Regression models
Batch processing
Feedback
Classrooms
Information technology
Acceso en línea:Citation/Abstract
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Resumen:This study explored the use of big-data learning diagnosis systems in higher education English precision teaching. The traditional one-size-fits-all teaching model is inadequate in the globalized context, while big-data technology offers new reform opportunities. The study selected 120 university English majors as samples and collected multi-dimensional data to build a linear regression model. Results show that online learning duration, homework completion rates, and classroom interaction frequency positively impact final grades. Targeted interventions lead to significant improvements in these areas. However, challenges remain in data security, teacher capability, system integration, and data quality. This study confirms the effectiveness of big-data learning diagnosis systems in enhancing precision teaching and provides valuable insights for future educational reforms.
ISSN:1548-7717
1548-7725
1098-8580
1537-937X
DOI:10.4018/JCIT.386383
Fuente:ABI/INFORM Global