Work

Analyzing the learning behavior patterns of business english learners using deep learning technology

Year: 2025

Type: article

Abstract: This study employs deep learning technology to conduct a comprehensive analysis and prediction of learning behavior patterns among business English learners, making several innovative contributions. First, it applies a hybrid deep learning approach, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model both static and temporal aspects of learning behaviors. Second, the study identifies novel patterns, such as the strong correlation between high-frequency evening study sessions and improved academic performance , providing data-driven insights into effective learning strategies. Third, it demonstrates the feasibility of leveraging deep learning to dynamically adjust learning paths and offer real-time personalized learning recommendations, significantly enhancing learner engagement and outcomes. These findings lay the groundwork for integrating deep learning into intelligent education systems and highlight its potential to revolutionize personalized learning in the field of business English education.

Source: Systems and Soft Computing

DOI: 10.1016/j.sasc.2025.200259

Author: Xiaohui Zeng

Institution: Xi'an Fanyi University

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $1,870