Journal of Applied Science and Engineering

Published by Tamkang University Press

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Feedback-Driven Adaptive English Language Learning Framework Through Hidden Engagement Pattern Analysis Using CGSPQ-Learning And GH-Fuzzy

 2026

 Volume 32

Shujuan Ma

School of Humanities and Law, Zhengzhou Technology and Business University, Zhengzhou 450018, China

Received: March 10, 2026
Accepted: April 14, 2026
Publication Date: June 8, 2026

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English is widely used globally, but traditional methods fail to analyse hidden engagement patterns, leading to inaccurate interpretation of learner behaviour. Hidden engagement reflects implicit interactions that distinguish productive learning, involving active participation and measurable improvement, from passive usage with limited outcomes. To address this, a feedback-driven adaptive English learning framework is proposed using
GH-Fuzzy and CGSPQ-Learning. The process begins with data collection, preprocessing, augmentation, and attribute extraction, followed by learning plateau detection and interrelationship analysis. GH-Fuzzy is employed to analyse hidden engagement patterns and differentiate learner behaviours. Student improvement is then classified using IQBiSLSTM for enhanced stability and efficiency. Finally, CGSPQ-Learning generates personalized feedback to improve learning outcomes. The proposed model outperforms existing methods, achieving improved performance with reduced rule generation time.

Keywords: Feedback-Driven Model, Adaptive English Language Learning, Productive and Passive Learning, Student

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