Advanced Models for Predicting Student Performance in Higher Education using Machine Learning and Data Mining Techniques

In techniques of level predictions for students, it is important in the higher learning institutions since it provides an insight to the institution that a certain student is in a vulnerable position so that closer monitor can be taken on him or her. Several machine learning methodologies and data mining strategies for estimating the academic performance are presented in this paper. We also incorporate the findings of new studies to related models like decision tree, support vector machines and deep learning models and data sets in the field of education. We also talk about the effect of strategies and amplified assessment systems based on IoT for better student performance. We further observe that by identifying these students at an early stage and applying these techniques, learning achievement and dropout rates can be improved dramatically.