Improving Performance of Smart Education Systems by Integrating Machine Learning on Edge Devices and Cloud in Educational Institutions

Abstract

Educational institutions today are embracing technology to enhance education quality through intelligent systems. This study introduces an innovative strategy to boost the performance of such procedures by seamlessly integrating machine learning on edge devices and cloud infrastructure. The proposed framework harnesses the capabilities of a Hybrid 1D Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) architecture, offering profound insights into intelligent education. Operating at the crossroads of localised and centralised analyses, the Hybrid 1D CNN-LSTM architecture signifies a significant advancement. It directly engages edge devices used by students and educators, laying the groundwork for personalised learning experiences. This architecture adeptly captures the intricacies of various modalities, including text, images, and videos, by harmonising 1D CNN layers and LSTM modules. This approach facilitates the extraction of tailored features from each modality and the exploration of temporal intricacies. Consequently, the architecture provides a holistic comprehension of student engagement and comprehension dynamics, unveiling individual learning preferences. Moreover, the framework seamlessly integrates data from edge devices into the cloud infrastructure, allowing insights from both domains to merge. Educators benefit from attention-enhanced feature maps that encapsulate personalised insights, empowering them to customise content and strategies according to student learning preferences. The approach bridges real-time, localised analysis with comprehensive cloud-mediated insights, paving the path for transformative educational experiences. Empirical validation reinforces the effectiveness of the Hybrid 1D CNN-LSTM architecture, cementing its potential to revolutionise intelligent education within academic institutions. This fusion of machine learning across edge devices and cloud architecture can reshape the educational landscape, ushering in a more innovative and more responsive learning environment that caters to the diverse needs of students and educators alike.