Analysis of Student Academic Performance through Expert systems

Predicting the performance of students is one of the most important topics required for learning contextssuch as colleges and universities, as it helps to design successful mechanisms that boost tutorial outcomesand prevent dropouts among various items. These are benefited by automating the many processes involvedin the activities of usual students which handle huge volumes of information collected from package toolsfor technology-enhanced learning. Thus, the careful analysis and interpretation of these information wouldprovide us with valuable data regarding the data of the students and therefore the relationship betweenthem and hence the tutorial tasks. This data is the supply which feeds promising algorithms and methodsable to estimate the success of the students. During this analysis, virtually many papers were analysed toshow radically different trendy techniques widely applied to predict the success of students, along with thegoals they need to achieve in this area. These computing-related techniques and approaches are mainlymachine learning techniques, deep learning techniques, Artificial Neural Networks & Neural NetworksConvolution, etc. This paper demonstrates the analysis and their comparisons of various methods used toforecast Student Academic success.