Fuzzy association rule mining for Personalized English Language Teaching from higher education

Abstract

College students are expected to possess a higher level of education, and increasing numbers of job positions require higher proficiency in English. Students come from various wisdom, and their demands for learning, ways of thinking, and learning methods will vary. In higher education, Personalized English Language Teaching (PELT) is crucial for addressing the various requirements of students, boosting motivation with involvement, maximizing resource use, preparing them for future professions, and improving their learning outcomes. To provide students with personalized and effective training in higher education, Fuzzy associated Frequent Pattern-Growth (F2PG-PELT) has been proposed to discover personalized relationships and patterns in student data using fuzzy association rule mining. Secondly, the Frequent Pattern-Growth algorithm is used to identify frequent patterns in learner data that are pertinent and meaningful. The relationships and connections between language factors or student qualities are crucial to personalized education and can be identified using a candidate frequent pattern tree. Due to the diversity of teaching strategies, the fuzzy association rules are generated using minimum support and confidence measure. The proposed model develops high learning outcomes with self-assurance, improves language and communication skills, offers individualized learning opportunities, and fosters lifelong learning abilities. The experimental outcomes demonstrate that the suggested fuzzy association rule mining employed for the PELT model increases the student engagement ratio, teaching efficiency ratio, learning outcome ratio, and teacher involvement ratio compared to other state-of-the-art approaches.