Version 1
: Received: 18 September 2024 / Approved: 18 September 2024 / Online: 18 September 2024 (11:37:40 CEST)
How to cite:
Xia, Y.; Shin, S.-Y.; Shin, K.-S. Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis. Preprints2024, 2024091381. https://doi.org/10.20944/preprints202409.1381.v1
Xia, Y.; Shin, S.-Y.; Shin, K.-S. Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis. Preprints 2024, 2024091381. https://doi.org/10.20944/preprints202409.1381.v1
Xia, Y.; Shin, S.-Y.; Shin, K.-S. Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis. Preprints2024, 2024091381. https://doi.org/10.20944/preprints202409.1381.v1
APA Style
Xia, Y., Shin, S. Y., & Shin, K. S. (2024). Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis. Preprints. https://doi.org/10.20944/preprints202409.1381.v1
Chicago/Turabian Style
Xia, Y., Seong-Yoon Shin and Kwang-Seong Shin. 2024 "Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis" Preprints. https://doi.org/10.20944/preprints202409.1381.v1
Abstract
This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model's adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences.
Keywords
Personalized Learning; Big Data Analytics; Language Acquisition; Educational Technology; Learner Engagement
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
