Centralized Student Placement Performance Monitoring and Automated Student Ranking System Using Real-time Data From Coding Platforms and Github
Year: 2024
Type: article
Abstract: The traditional resume-based campus recruitment process gives little insight into a candidate's coding, problem-solving, and technical abilities.With employers preferring actual problem-solving abilities and open-source contributions, there is a growing demand for an automated, data-based student assessment system.The system integrates real-time data from LeetCode, GitHub, academic records, and internal placement exams to produce an objective ranking that is refreshed every 24 hours.Recruiters are able to shortlist candidates effectively on the basis of coding skills, project contributions, and certifications, thus eliminating manual work and increasing accuracy.Placement coordinators are also given insights into student performance so that they can provide personalized help.The use of LeetCode and GitHub APIs ensures scalability along with fairness and transparency in the selection.The ranking process motivates students to constantly develop by monitoring current performance instead of using stagnant CV information.Furthermore, recruiters are able to filter candidates according to skill needs, coding problems resolved, and open-source contributions, allowing for more accurate hiring.Future developments might involve AIpowered candidate profiling for better recruitment optimization.This system automates campus recruitment, rendering it more efficient, data-driven, and impartial.
Author: Poovarasan S
Language: English
Topic: AI and HR Technologies
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4408009524