Improved Accuracy by Novel Inception Compared over GoogleNet in Predicting the Performance of Students in Online Education During COVID

Abstract

The goal of this research is to enhance the accuracy of predicting students' performance in online education during the Covid-19 pandemic by comparing the Novel Inception algorithm with the GoogleNet algorithm. Materials and Methods: The current research paper investigates the performance of two distinct algorithms, namely the Novel Inception algorithm and the GoogleNet algorithm, in two separate groups with 20 samples in each group. The statistical significance of the collected data was assessed using SPSS with a G-power value set at 85%. The study also explores the accuracies of these algorithms with varying sample sizes. Result: Inception algorithm provides a higher accuracy of 91.0480% when compared to GoogleNet algorithm with accuracy of 89.8860% in predicting the Performance of Students in online education during covid. With a significance value of p=0.007 (p<0.05) which comparison of Novel Inception algorithm compared over GoogleNet algorithm in preding the Performance of Students in online education with improved Accuracy. The research findings indicate that the performance of students in online education during COVID-19 can be better predicted using the Novel Inception algorithm than the GoogleNet algorithm. The accuracy of the Novel Inception algorithm was observed to be higher as compared to the GoogleNet algorithm.

Keywords

• covid
• education
• e-learning
• googlenet
• machine learning
• novel inception
• online education
• student