An Intelligent Feedback Mechanism for Mobile English Learning Based on Learning Behavior Analytics.

Saved in:

Bibliographic Details
Title:	An Intelligent Feedback Mechanism for Mobile English Learning Based on Learning Behavior Analytics.
Authors:	Cai, Jingyi¹ caijingyi1984@163.com
Source:	International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 4, p90-104. 15p.
Subjects:	Learning analytics, Metacognition, Individualized instruction, Psychological feedback, Behavioral assessment, Mobile learning, English language education
Abstract:	The rapid proliferation of mobile learning technologies has reshaped English learning contexts, giving rise to fragmented learning patterns and increasing demands for personalization. However, feedback mechanisms in current mobile English learning systems remain overly focused on knowledge error correction, with limited consideration of learners' cognitive processes, thereby constraining learning effectiveness. The deep integration of learning behavior analytics with intelligent feedback is regarded as a promising pathway to address this limitation. This study aims to address a central research question: how can fine-grained learning behavior analytics be leveraged to integrate observable behavioral data with latent metacognitive states in order to construct a pedagogically adaptive and personalized intelligent feedback mechanism for mobile English learning? To this end, a multi-task deep knowledge tracing (DKT) approach incorporating metacognitive assessment was proposed. On this basis, a closed-loop framework integrating data perception, joint modeling, and multidimensional feedback was constructed, with optimization strategies tailored to mobile learning scenarios. Experimental results based on real-world mobile learning data demonstrated that the proposed approach significantly outperformed conventional DKT models in both knowledge tracing accuracy and metacognitive state recognition accuracy. Moreover, the resulting intelligent feedback mechanism effectively enhanced learners' English learning performance and metacognitive abilities. This study extends existing language learning theory through the integration of metacognitive modeling with knowledge tracing, introduces a discipline-adaptive multi-task intelligent feedback modeling paradigm, and provides a practical pathway for the intelligent enhancement of mobile English learning systems. [ABSTRACT FROM AUTHOR]
	Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	The rapid proliferation of mobile learning technologies has reshaped English learning contexts, giving rise to fragmented learning patterns and increasing demands for personalization. However, feedback mechanisms in current mobile English learning systems remain overly focused on knowledge error correction, with limited consideration of learners' cognitive processes, thereby constraining learning effectiveness. The deep integration of learning behavior analytics with intelligent feedback is regarded as a promising pathway to address this limitation. This study aims to address a central research question: how can fine-grained learning behavior analytics be leveraged to integrate observable behavioral data with latent metacognitive states in order to construct a pedagogically adaptive and personalized intelligent feedback mechanism for mobile English learning? To this end, a multi-task deep knowledge tracing (DKT) approach incorporating metacognitive assessment was proposed. On this basis, a closed-loop framework integrating data perception, joint modeling, and multidimensional feedback was constructed, with optimization strategies tailored to mobile learning scenarios. Experimental results based on real-world mobile learning data demonstrated that the proposed approach significantly outperformed conventional DKT models in both knowledge tracing accuracy and metacognitive state recognition accuracy. Moreover, the resulting intelligent feedback mechanism effectively enhanced learners' English learning performance and metacognitive abilities. This study extends existing language learning theory through the integration of metacognitive modeling with knowledge tracing, introduces a discipline-adaptive multi-task intelligent feedback modeling paradigm, and provides a practical pathway for the intelligent enhancement of mobile English learning systems. [ABSTRACT FROM AUTHOR]
ISSN:	18657923
DOI:	10.3991/ijim.v20i04.60517