Automated Alumni Employment Tracking System Utilizing LinkedIn Data
Noorul Sama Sahel
Co-Presenters: Individual Presentation
College: The Dorothy and George Hennings College of Science, Mathematics and Technology
Major: Computer Science
Faculty Research Mentor: Daehan Kwak
Abstract:
This research focuses on developing an automated system to collect and track employment data of Kean University alumni using LinkedIn. Keeping alumni records up to date is essential for strengthening professional networks, improving career services, and supporting institutional analysis. However, traditional surveys and institutional records often result in outdated or incomplete data. To address this challenge, this study leverages web automation tools to efficiently extract real-time employment information from LinkedIn.The system is built using Python and Selenium, allowing for automated interactions with LinkedIn, including logging in, searching for alumni, and extracting relevant employment data. Two approaches were evaluated: direct querying by searching for “Kean University” on LinkedIn and a pre-compiled alumni list for targeted searches. The pre-compiled alumni list method proved more reliable, minimizing irrelevant results and improving data accuracy.Despite its effectiveness, the system encountered challenges such as CAPTCHA verifications, restricted profile access, and duplicate name entries, which affected data completeness. Solutions like randomized browsing behavior and automated CAPTCHA handling were implemented to address these issues. The findings demonstrate that automating alumni data collection is feasible and significantly improves efficiency compared to manual tracking methods.Future work will focus on refining data validation techniques, improving scalability, and integrating additional data sources beyond LinkedIn to create a more comprehensive alumni employment tracking system. This study highlights the potential of automation in higher education institutions for maintaining accurate and up-to-date alumni records.