Article: Design of interactive English learning system driven by big data Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.35 pp.57 - 80 Abstract: As the user base expands, recommendations under high concurrency conditions cannot meet the low latency requirements. In order to improve the accuracy of recommendations and the real-time performance of the system, a big data-driven interactive English learning system is designed. The hardware part deployed a distributed server cluster (16 core CPU/128 GB memory nodes × 8), equipped with NVIDIA Tesla T4 GPU accelerated deep learning inference, and implemented elastic resource scheduling through Kubernetes containerisation platform; at the software level, a microservice architecture is adopted, with an adaptive interactive interface built on React in the front-end and Spring Cloud framework in the back-end. Constructing dynamic learning profiles through multi-source data fusion, combined with an improved K-means clustering algorithm to achieve precise learner clustering. At the recommendation algorithm level, collaborative filtering is integrated with cognitive ability resource difficulty matching (DSM) and preference resource type correlation (RSM) to generate TOP-N recommendation sets through weighted standardisation, achieving teaching resource recommendation. The experimental results show that this scheme effectively solves the problem of recommendation quality degradation in large-scale user scenarios, while ensuring real-time interactive performance, providing practical reference for the technical optimisation of online education systems. Inderscience Publishers - linking academia, business and industry through research

Title: Design of interactive English learning system driven by big data

Authors: Juan Wu

Addresses: Hainan Vocational University of Science and Technology, 18 Qiongshan Avenue, Meilan District, Haikou City, Hainan Province, China

Abstract: As the user base expands, recommendations under high concurrency conditions cannot meet the low latency requirements. In order to improve the accuracy of recommendations and the real-time performance of the system, a big data-driven interactive English learning system is designed. The hardware part deployed a distributed server cluster (16 core CPU/128 GB memory nodes × 8), equipped with NVIDIA Tesla T4 GPU accelerated deep learning inference, and implemented elastic resource scheduling through Kubernetes containerisation platform; at the software level, a microservice architecture is adopted, with an adaptive interactive interface built on React in the front-end and Spring Cloud framework in the back-end. Constructing dynamic learning profiles through multi-source data fusion, combined with an improved K-means clustering algorithm to achieve precise learner clustering. At the recommendation algorithm level, collaborative filtering is integrated with cognitive ability resource difficulty matching (DSM) and preference resource type correlation (RSM) to generate TOP-N recommendation sets through weighted standardisation, achieving teaching resource recommendation. The experimental results show that this scheme effectively solves the problem of recommendation quality degradation in large-scale user scenarios, while ensuring real-time interactive performance, providing practical reference for the technical optimisation of online education systems.

Keywords: big data-driven; interactive; English learning; resource recommendation; user portrait.

DOI: 10.1504/IJICT.2026.153003

International Journal of Information and Communication Technology, 2026 Vol.27 No.35, pp.57 - 80

Received: 05 Dec 2025
Accepted: 06 Jan 2026
Published online: 17 Apr 2026 *

Title: Design of interactive English learning system driven by big data

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