Survival Study on Student Academic Performance Classification based on Mental Health

Abstract

Educational Data Mining (EDM) is used to extract the important information from educational data. EDM identifies the trends from educational data to enhance the student academic performance. EDM uses the machine learning conceptsto recognize the learning, to improve teaching and to optimize the educational systems. Mental health issues are prevalent among students. Depression has significant obstacle for performing the long-term learning in educational system. Student dropout prediction is an important event for educational institutions and policymakers around world. Early student academic performance prediction is an essential research topic in educational data mining. Different deep learning and artificial intelligence methods are introduced to forecast the student academic performance. However, the existing prediction techniques failed to handle the student mental health and their mood changes. In order to address the existing issues, different artificial intelligence and deep learning methods is introduced for student academic performance classification based on mental health.