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Abstract This research delves into the effective integration of Information Communication Technology (ICT) methods within English Language Teaching (ELT) for technical students. It investigates strategies to optimize ICT tools for enhancing language learning in technical education contexts. The study aims to explore practical approaches that leverage ICT resources to improve language proficiency among technical students, considering their unique learning needs and environments. Through this investigation, it seeks to uncover best practices for seamlessly integrating ICT into English language instruction tailored to technical education settings. The paper introduces Information and Communications Technology with machine learning algorithms (ICT-MLA), a framework designed for enhancing the online English teaching audit process. This framework aims to simulate teaching methodologies that align with the specific needs of online English teaching. Addressing the significance of ICT integration in ELT, the article focuses on identifying potential obstacles in the integration process. It emphasizes leveraging analytical data and reporting through a Hybrid Learning Management System (HLMS) to pinpoint training and learning gaps. The study proposes the use of Information Gain (IG) as a feature selection method to reduce noise and enhance classifier influence by identifying relevant features for language learning outcome prediction or analysis. Additionally, the paper introduces a novel Hedge Backpropagation (HBP) method aimed at effectively updating neural network parameters in an online setting. The efficacy of this method is validated across diverse large-scale datasets, encompassing both stationary and concept-drifting scenarios. The overall accuracy value using the Python tool is 98.6% which is better than existing methods.
Binu (Mon,) studied this question.