Analysis of Factors Influencing Students' Academic Challenges and their Impact on Outcomes

Abstract

The primary goal of educational institutions is to offer a favorable learning environment and impart valuable knowledge to their students. The student’s achievement relies on academic performance and it is affected by the psychological problems encountered during the studies. In this paper, we conducted an analysis of the psychological issues that characterize students' experiences, including heavy workloads, insufficient or excessive sleep, mental stress, depression, feelings of pressure, diversity-related issues, negative emotions, and other study-related problems. We then performed a classification to determine the level of stress experienced by the students and examined its impact on academic performance. The dataset used in current work is collected using research methodology, and performed preprocessing. A model is developed to classify/predict the stress level. Classification and prediction techniques employed are Random Forest, K-nearest neighbors, Naïve Bayes, SVM, and ANN. We compared the performance using the metrics accuracy, precision, and recall. According to our extensive experiments, the accuracy of the Random Forest model is 98.16%, which demonstrates superior performance compared to Naïve Bayes (96.78%) and k-NN (95.41%). The ANN model accuracy is 98.16%. The Random Forest model performance is best as compared to other. Statistical method used to find the impact of students’ stress level on academic achievement.