ItemArticle

Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization

Title
Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization
Creator
Ramesh, Komminni; Thomas, Christine Ann; Osei-Asiamah, Joel; Pagidipati, Bhuvaneswari; Muniyandy, Elangovan; Reddy, B. V. Suresh; El-Ebiary, Yousef A. Baker
Description
Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for English as a second language (ESL). The following research steps include simplifying system complexity while adding multimodal learning signals including eye monitoring and speech recognition and further developing the model across various educational subject areas. The research works as a promising foundation which propels AI real-time adaptive education systems for students from various backgrounds. (2025), (Science and Information Organization). All Rights Reserved.
Source
International Journal of Advanced Computer Science and Applications;Volume;16;Issue;4;pp.564-576
Date
01-01-2025
Publisher
Science and Information Organization
Subject
adaptive content personalization; artificial intelligence-based English as a secondary language learning; Cognitive load management
Coverage
Ramesh K., BoS, Anurag Engineering College, Suryapet District, Telangana, Kodad, India; Thomas C.A., Department of English and Cultural Studies, Christ University, Bengaluru, India; Osei-Asiamah J., Department of Science and Technology Education, University of South Africa (Unisa), Gauteng Province, Pretoria, South Africa; Pagidipati B., Dept. of English and Foreign Languages, Sagi Rama Krishnam Raju Engineering College (A), West Godavari Dt, Andhra Pradesh, Bhimavaram, 534204, India; Muniyandy E., Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India, Applied Science Research Center, Applied Science Private University, Amman, Jordan; Reddy B.V.S., Department of CSE, Koneru Lakshmaiah Education Foundation, AP, Vaddeswaram, India; El-Ebiary Y.A.B., Faculty of Informatics and Computing, UniSZA University, Malaysia
Rights
All Open Access; Gold Open Access
Relation
ISSN: 2158107X;
Format
online
Language
English
Type
Article

Citation

Ramesh, Komminni; Thomas, Christine Ann; Osei-Asiamah, Joel; Pagidipati, Bhuvaneswari; Muniyandy, Elangovan; Reddy, B. V. Suresh; El-Ebiary, Yousef A. Baker, “Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/23268.

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