Year: 2024
Type: conference-paper
Abstract: Graduate enrollment and admission work are crucial to the higher education system and essential for maintaining academic heritage and innovation. With the rapid advancement of information technology (IT), especially as China's education sector enters a new stage of comprehensive digital transformation, universities face new development opportunities and severe challenges. The traditional enrollment information management system has become outdated regarding processing speed, information security, and intelligent decision-making assistance. It cannot meet the current situation of expanding graduate enrollment scale, complex processes, and diversified needs of candidates. Therefore, this article proposes an innovative design scheme for a graduate enrollment information management system based on machine learning (ML) technology. This solution integrates advanced data mining (DM), natural language processing (NLP), and predictive analysis algorithms to achieve automated collection, intelligent screening, precise matching, and efficient operation of enrollment information. Experimental results have shown that the system effectively improves the efficiency and quality of graduate enrollment work.
Authors: Kainan Li, Hailin Jiang
Institution: Changchun University of Chinese Medicine
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4412524659