Abstract:
Current higher educational institutions face significant challenges in meeting the demand for on-site student support services due to increasing student enrollment and budget constraints. Advancements in technology offer promising solutions through support systems that could be made available online. This research aims to design a readily available Comprehensive Student Support System for undergraduate students, which provides personalized support in multiple facets. Main objectives of the study are to provide proactive mental health support, enable career awareness and readiness, improve academic participation out of the course-curriculum, and to identify the best tools and technologies for system implementation. The system's main components include proactive support for mental well-being through the practice of diary writing, job recommendations based on the resume data to help students identify their current job placement status, career path guidance that supports students to discover new avenues available based on their interests, and facilitating networking opportunities for professional growth through academic related external event recommendation. Utilizing advanced methods in Natural Language Processing and Machine Learning has proven effective in achieving the objectives. The system is evaluated using Functional and User Acceptance Testing methods. Key insights from the system's evaluation highlight its usability and acceptance as a student support system.
