The Performance Evaluation of E-learning During the Emergency Using Machine Learning

E-learning is one of the educational alternatives available to students who need assistance during an emergency. (e.g., Covid-19 pandemic, bad climate, etc.). Most educational institutions are moving a significant portion of their curriculum toward an online learning paradigm to reduce the amount of face-to-face interaction between students and faculty members during times of emergency (e.g., in the case of Covid-19 pandemic). The success of E-learning is conditional on a wide range of aspects, such as students’ and teachers’ levels of self-efficacy, attitudes toward, and confidence in making use of the relevant technology; the instructional approaches that are utilized; the capacity to monitor and evaluate educational outcomes; and students’ levels of motivation. The performance and circumstances of students who are engaged in e-learning are analyzed in this paper. The research investigates and evaluates predictions made by a model that is based on machine learning techniques. Predicting the degree to which students are delighted with the online mode of instruction by considering several parameters, including internet capability, and involvement in the online mode of instruction.