An integrated system for predicting students' academic performance in smart universities.

Due to the current situation with Coronavirus (COVID-19) the attendance of students in the academic life has changed and the educational process has been driven towards smart educational environments Higher educational Institutes invest significant resources in reforming their educational programs so that it will support distance learning using asynchronous or synchronous methodologies and tools In this work, we propose the development of a student profile using data from both asynchronous and synchronous e-learning platforms, using a multi-layered neural network in order to classify students\u0027 performance A neural network is compared against Support Vector Machines, k-Nearest Neighbour and decision trees The results indicate that the Neural network achieves better accuracy than the others, so using our methodology the instructors or the policy makers of the institute will be able to keep informed about the performance of the students, or take the appropriate actions in order to prevent student failure or low participation © 2020 ACM