Title: Algorithm and data model for analysis of data to enhance online learning using graph mining techniques

Abstract

Nowadays, the online education advancement has maximized after the COVID pandemic. Hence, improving the facilities and response is much important task in the digital applications. So, several neural models have been implemented alone and in combined hybrid version. However, those approaches have increased the computation cost and complexity because of the vast unstructured data. In addition, the unstructured data contains much amount of noise features that has made difficulties during the data analysis process. Also, if too many data are entered as the same time, then data overloading has been recorded during the grade analyzing process. ENAM Preprocessing Students data Database Average Bad Good Performance Prediction Feature Analysis Fig.1 Proposed architecture Hence, the data overloading is the main cause of several issues like transmission delay, high resource usage and malicious events vulnerability. These issues have motivated this research toward on implementing the intelligent Apriori model. Hence, the present study has aimed to develop a novel Elman Neural with Apriori Mining (ENAM) to enhance the online education system by increasing the rapidity score on analyzing the student performance. Initially, the data has been pre- processed and entered to the classification module then the feature extraction and classification process has been performed. Finally, based on the present grade in the trained datasets, the student’s performance has been noted. Subsequently, the parameters of the designed model have been validated and compared with other models, the proposed architecture is described in fig.1. In addition, the model Apriori Mining has afforded the student’s performance results based on the priority of the grade submissions. This helps to avoid the data overloading and security threat. The performance parameter that has considered in this research work is Recall, F-measure, Precision, error rate and accuracy. Hence, by implementing the ENAM model in the online education system, the communication and data analysis process became enhanced with rapid accurate validation.