Fuzzy association rule mining for personalized the Chinese language teaching from higher education

Abstract

Chinese Language learning grows ever more essential to develop the students’ personalities and values as the curriculum, thereby improving teaching strategies based on students’ learning preferences are more crucial. Students’ participation in learning the Chinese Language is generally minimal and typically operates in a passive learning mode. The development of the Chinese Language instruction in these higher educational settings will be impacted by the absence of an organized strategy for teaching the Chinese Language. An algorithm is called the Fuzzy Pattern-driven Personalized Teaching (FPPT) has been proposed to identify the association between the students learning patterns and interests in the Chinese Language in the higher education for providing the personalized teaching to solve these challenges. Fuzzy sets are incorporated into FP-Growth for personalized the Chinese Language learning to improve the suggestions by considering the ambiguous preferences and the proficiency levels. The fuzzy pattern is unrevealed by implementing the Frequent Pattern (FP) growth algorithm to find patterns in the students learning activity and preferences so that personalized the teaching methods can be developed to meet the needs of each student and maximize their motivation for the language learning. Using the support and the confidence measures, these identified Fuzzy association relationships of student learning interest results in personalized the Chinese Language teaching in the higher education. The experimental results showed that the proposed FPPT system significantly improved each student’s learning outcome, communication effectiveness, learning motivation, and the Language proficiency level.