Predicting Student Performance and Its Impact on Mental Health Using Machine Learning
Author: Harsimran Singh
Publisher:
Published: 2023
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKToday, the main aim of educational institutes is to provide a high level of education to the students. Career selection is one of the most important and quite difficult decisions for learners. It is very important to examine student's capabilities and interests. As stresses of tests, peer and parental pressure on marks scored and job opportunities are some of the factors that lead to mental illness for university students. Determining the factors underlying mental illness from academic success to maintain the proper balance of life is becoming increasingly necessary. This kind of novel machine learning prediction system would help students studying in engineering institutes to address these key challenges So that they will focus on their targeted carrier. In this study, both classification and clustering techniques have been tested on the student academic and family datasets of various engineering students in Delhi, India. Although all the classifier models show comparably high classification performances, the Hybrid neural network is the best-concerning accuracy and precision. In addition, the analysis shows that mental health based on the performance of the students depends on various factors. The findings of this paper indicate the effectiveness and expressiveness of data mining models in performance evaluation. The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in the prediction of academic performance as well as mental wellnesses of the students.
