English Network Teaching Model and Design of Evaluation System Based on Association Rule Algorithm

• Published in: International journal of information and communication technology education Vol. 20; no. 1; pp. 1 - 22
• Main Authors: Sun, Xueyu, Wang, Ting
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 17.07.2024
• Subjects: Academic Achievement; Algorithms; Career Development; College English; Computer Mediated Communication; Consumer Economics; Correlation; Data analysis; Data mining; Distance Education; Distance learning; Education; Educational Change; Educational technology; Electronic Learning; Influence of Technology; Learning Processes; Machine learning; Measurement Techniques; Multivariate analysis; Networks; Periodicals; Quality of education; Teaching methods; Teaching Models; Technology Education; Test Results
• ISSN: 1550-1876; 1550-1337; 1550-1337
• DOI: 10.4018/IJICTE.349007

This study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an “interest” parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's efficacy by correlating learning content with academic performance, specifically CET-4 and CET-6 scores. Comprehensive preprocessing ensures data quality and privacy, employing techniques like de-identification, data perturbation, and aggregation. Advanced data analysis, including cross-validation and multivariate techniques, bolsters findings' reliability. Results highlight the ARM model's capacity to generate personalized learning paths, transcending conventional methods, and its potential as a cornerstone for data-driven education reforms. Future research will explore machine learning refinements and cultural adaptability to broaden its impact, fostering equitable, high-quality digital English education worldwide.