Research on personalized Japanese language teaching model based on big data technology

Abstract In order to better improve students’ Japanese language performance and help them progress, this paper proposes an analysis of personalized teaching in a big data technology environment. We build model learner characteristics use collaborative filtering techniques push learning information from learners with the same or similar interest preference characteristics. Adjust strategies based on visual user panel. Build data, extract visualize each student’s behavior, find out gap between objectives preset ones, record methods. Based dynamically update content, assign most appropriate tasks, determine distance objectives, ability effectiveness. Through inductive inference training dataset, probabilities all subsets are calculated, relevant subset intervals merged, business sensitivity index is introduced measure whole source, which makes improved decision tree gradually reduce uncertainty division decrease weight degree attributes, better. The results showed that reached 71% accuracy 80 cases stayed above 73% afterwards, ratio mastered knowledge number knowledges learned was 0.4. Personalized under method led significant improvement performance.