Abstract
Education 4.0 contemplates the involvement of emerging technologies like IoT (Internet of Things), Fog and cloud computing. Ubiquitous real-time monitoring in the challenging environment of an educational institution is the key requirement for proper implementation of Education 4.0 around the world. Conspicuously, this research presents a novel monitoring and irregularity detection framework for educational institutions. In this research, acquisition of data while deploying IoT is being suggested in the comprehensive environs of educational institutions. Pre-processing of acquired data for feature extraction is implemented using the Fog-Cloud nodes. Subsequently, the Temporal Chunk (TC), which is articulated with Temporal Data Mining, is further employed to detect irregularities based on SoTL (Set of threshold limits) and HAA (Historical Adversity Approximation). Successively, a Multi-Layered Bi-Directional Long Short Term Memory (M-Bi-LSTM) oriented irregularity prediction model is deployed. Furthermore, a fog-inspired alert generation and reporting module are employed for real-time reporting to notify the concerned stakeholder for in- time preventive action corresponding to a predicted irregularity. To authenticate the proposed framework and to avoid the experimental implementation cost, experimental simulations are performed. The experimental results verify that the proposed framework is capable of performing better in comparison to other contemporary decision-making methods for delay efficiency, data classification, irregularity prediction, and system stability. Moreover, the proposed framework can successfully estimate most of the irregularities in an educational environment and provide support for the proper implementation of Education 4.0 in a proficient and realistic manner.