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

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

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to better improve students’ Japanese language performance and help them progress, this paper proposes an analysis of personalized Japanese language...

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

Data publikacji: 02 cze 2023

Zakres stron: -

Otrzymano: 19 lip 2022

Przyjęty: 21 paź 2022

DOI: https://doi.org/10.2478/amns.2023.1.00192

Słowa kluczowe
Big data technologies, Personalized instruction, Improved decision tree models, Instructional strategies, Collaborative filtering techniques

© 2023 Huaigu Huang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 02 cze 2023

Zakres stron: -

Otrzymano: 19 lip 2022

Przyjęty: 21 paź 2022

DOI: https://doi.org/10.2478/amns.2023.1.00192

Słowa kluczoweBig data technologies, Personalized instruction, Improved decision tree models, Instructional strategies, Collaborative filtering techniques

© 2023 Huaigu Huang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Big data technologies, Personalized instruction, Improved decision tree models, Instructional strategies, Collaborative filtering techniques