Analysing student performance for online education using the computational models

Traditional face-to-face education has shifted to online education to prevent large gatherings and crowds from spreading the COVID-19 virus. Several online platforms like Zoom, GoToMeeting, Microsoft Teams, and WebEx restore traditional teaching and promote online education. Online learning classes are particularly beneficial for hospitalized students, massive open online courses (MOOCS), and lifelong learners. This paper uses the deep learning model to predict student performance in an online environment. Student interaction with the online environment is vital to predicting student performance. This prediction will help identify at-risk students, and teachers can help motivate the poor-performance students. We used student interaction features like click sums. We studied credits to understand the students' behaviour and tried to forecast the outcomes of their final scores by using the hybrid deep learning models. The proposed hybrid model predicts student performance with an accuracy of 98.80%. The results proved that the proposed deep learning model effectively predicts student performance in an online environment.