{"id":7737,"date":"2026-06-08T10:46:44","date_gmt":"2026-06-08T02:46:44","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7737"},"modified":"2026-06-08T11:02:17","modified_gmt":"2026-06-08T03:02:17","slug":"jase-202609-32-073","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-073","title":{"rendered":"Feedback-Driven Adaptive English Language Learning Framework Through Hidden Engagement Pattern Analysis Using CGSPQ-Learning And GH-Fuzzy"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Shujuan Ma<\/i><\/a><\/p>\n\n\n\n

School of Humanities and Law, Zhengzhou Technology and Business University, Zhengzhou 450018, China<\/p>\n<\/div>\n\n\n\n

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Received: March 10, 2026
Accepted: April 14, 2026
Publication Date: June 8, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Structure of the Proposed Work <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.073<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

Keywords: Feedback-Driven Model, Adaptive English Language Learning, Productive and Passive Learning, Student<\/em><\/p>\n\n\n\n

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