Design a Platform for Sharing Educational Resources for Student Based on Dual Attention Graph Convolutional Networks

Abstract

A key component of raising educational standards is always the sharing of educational resources throughout higher education institutions. The development of digital and network infrastructure among colleges and universities can propel the sharing and exploitation of excellent educational resources to new heights. Nevertheless, there is a lack of an ideal platform for resource selection and integration in the current crop of information-based teaching tools. To overcome this drawback, the methodology used in this study was described in this research in order to classify student and teacher data more precisely. Ideological and Political Education (IPE) is the source of the data collection. The Modified Hamilton filter is used to preprocess the data during the pre-processing step. The excellent course videos, classroom performance, and interactions are all successfully classified by Dual Attention Graph Convolutional Networks (DAGCN).The neural network's weight parameter is optimized using the Lotus Effect Optimization Algorithm (LEA) to improve the DAGCN. The suggested DAGNN-LEA used with the MATLAB platform. The suggested approach was calculated using performance measures such as accuracy, precision, sensitivity, F-score, computation time, and recall. In comparison to the existing method, the suggested DAGNN-LEA method yields better results in terms of high accuracy 16.65%, 18.85%, and 17.89%, high sensitivity 16.34, 12.23%, and 18.54%, high precision 14.89%, 16.89%, and 18.23%, and low computing time 82.37%, 94.47%, and 87.76%.