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<p>This paper introduces AI intelligence technology in English distance education and establishes a learning analytics model that includes information collection and acquisition, data storage, cleaning, integration, analysis, visualization, and action. Each component corresponds to a series of specific technologies and processes, and generates information such as learner portrait modeling profiling learning behaviors, using data mining to provide personalized learning solutions for learning. The assessment system is constructed from three perspectives: contextual, cognitive, and technological, which ensures accurate teaching for the personalized needs of English learners. It has been verified that the contextual interaction effect on students’ performance accounts for 78% of the total sample in the English distance education system after applying AI intelligent technology. Students in the experimental class showed positive interest in the distance education system 47.44±7.15, which was significantly higher than that of the control class 29.65±8.21. This integration model provides a new development direction for English distance education, and also provides strong support for personalized teaching and continuous optimization of the educational process.</p>
</abstract>

This paper introduces AI intelligence technology in English distance education and establishes a learning analytics model that includes information collection and acquisition, data storage, cleaning, integration, analysis, visualization, and action. Each component corresponds to a series of specific technologies and processes, and generates information such as learner portrait modeling profiling learning behaviors, using data mining to provide personalized learning solutions for learning. The assessment system is constructed from three perspectives: contextual, cognitive, and technological, which ensures accurate teaching for the personalized needs of English learners. It has been verified that the contextual interaction effect on students’ performance accounts for 78% of the total sample in the English distance education system after applying AI intelligent technology. Students in the experimental class showed positive interest in the distance education system 47.44±7.15, which was significantly higher than that of the control class 29.65±8.21. This integration model provides a new development direction for English distance education, and also provides strong support for personalized teaching and continuous optimization of the educational process.

Research on the Integration Path and Practice of AI Intelligent Technology and English Distance Education

Applied Mathematics and Nonlinear Sciences

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

This paper introduces AI intelligence technology in English distance education and establishes a learning analytics model that includes information collection...

Research on the Integration Path and Practice of AI Intelligent Technology and English Distance Education

Pubblicato online: 09 dic 2023

Pagine: -

Ricevuto: 08 gen 2023

Accettato: 19 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01427

Parole chiave
AI Intelligent Technology, English Distance Education, Learning Analytics Model, Learner Profiling, Data Mining

© 2023 Lijie Jiang, published by Sciendo

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

Research on the Integration Path and Practice of AI Intelligent Technology and English Distance Education

Pubblicato online: 09 dic 2023

Pagine: -

Ricevuto: 08 gen 2023

Accettato: 19 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01427

Parole chiaveAI Intelligent Technology, English Distance Education, Learning Analytics Model, Learner Profiling, Data Mining

© 2023 Lijie Jiang, published by Sciendo

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

Parole chiave
AI Intelligent Technology, English Distance Education, Learning Analytics Model, Learner Profiling, Data Mining