Machine Learning Based Classification of Academic Stress Factors

This paper discusses the stress caused by activities related to academics, especially the stress experienced by new students. These days academics are one of the most dominant causes of stress in a student. Hence this paper proposes a system that would categorize students based on their stress levels. The system is built by combining the various questionnaires and scoring metrics that are famous worldwide and have been developed and backed by leading researchers.The basic structure of the proposed study is to build a system that presents a questionnaire with a defined scoring metric. Based on the answers received from the students, the system will be able to categorize the student’s stress levels. This research revolves around studying various parameters, psychological methodologies, and metrics that are currently being used to monitor users suffering from academic stress. Both global and local research papers and various publications have been studied to find the right set of assessments for the proposed system. For implementation, a questionnaire is used that asks the user various questions based on various parameters - Firstly, Student Academic Stress Scale (SASS). The scales evaluated are – Affective, Behavioural, Cognitive, and Physiological. Secondly, Stress in University Students (SUS) and lastly, Questionnaire of Academic Stress in Secondary Education (QASSE). The research is conducted on various colleges of UT Chandigarh, and results are discussed in the end.