Design of English Online Teaching System using Adaptive Crossover Mutation based Genetic Algorithm

With the enhancement of internet technology, the present education-based idea is significantly depending on ‘‘online” mode teaching. This teaching approach enhances the teaching effectiveness and to promotes the present education system progress. The conventional teaching approaches provides the issues like passive learning as well as minimum interest in learning. This research aimed to study how to determine and study the English online teaching system. Hence, this research proposes the Adaptive Crossover Mutation based Genetic Algorithm (ACMGA) for the English online teaching system. ACMGA has dynamically adapts to the unique learning pace as well as style of each student, designing personalized learning paths which optimize the educational experience. The attributes like main interface, teaching resources as well as auxiliary teaching attains the success rate at the number of 1000 users such as 95.28%, 94.28% and 94.13% respectively. The attributes like main interface, teaching resources as well as auxiliary teaching attains the response test at the number of 1000 users such as 1.47, 2.78 and 2.80 respectively.