Luận văn Thạc sĩ: Hệ thống quản lý và dự báo tuyển sinh tại Học viện Công nghệ Bưu chính Viễn thông

The rapid advancement of information technology (IT) has profoundly transformed various sectors, including education. Effective management and forecasting are critical for strategic planning, especially in university admissions. Historically, institutions like the Posts and Telecommunications Institute of Technology (PTIT) have relied on manual methods, such as Excel files, for managing student admissions. This approach is prone to errors, lacks integrated data analysis capabilities, and hinders proactive decision-making regarding resource allocation and enrollment strategies. This study addresses these challenges by developing an integrated system for admission management and forecasting. The primary objective is to enhance accuracy, provide timely insights into admission trends, and empower academic administrators with data-driven tools to optimize future admission campaigns and resource planning.

Cán bộ quản lý tuyển sinh, ban giám hiệu, và cán bộ giảng viên tại các trường đại học, đặc biệt là Học viện Công nghệ Bưu chính Viễn thông.

This Master's thesis presents the design, implementation, and evaluation of an "Student Admission Management and Forecasting System" specifically tailored for the Posts and Telecommunications Institute of Technology (PTIT). Recognizing the limitations of current manual, Excel-based admission processes, which are susceptible to errors and inefficiencies in data analysis and strategic planning, the study proposes a comprehensive IT solution. The research systematically investigates the existing admission workflow at PTIT, identifying key areas for improvement in data handling, reporting, and proactive decision-making. The core of the system lies in its dual functionality: robust admission management and advanced student admission forecasting. For the management component, the system facilitates efficient data entry (via forms or Excel import), streamlined information retrieval, and comprehensive statistical reporting of applicant and enrollment data. Critically, the forecasting module leverages cutting-edge data science techniques, particularly the Facebook Prophet algorithm, to analyze historical time series data related to admissions. This allows for the prediction of future enrollment figures, application trends, and student distribution across various majors and regions. Such predictive capabilities are vital for PTIT to anticipate resource needs, optimize admission quotas, and develop targeted marketing and recruitment strategies. The methodology adopted combines theoretical research with practical application development. It involves a detailed study of the Prophet algorithm's suitability for admission data, data collection from 2016-2021 at PTIT, and the subsequent evaluation of the system's performance. The application-oriented approach ensures that the developed system offers tangible value by minimizing administrative overhead, improving data accuracy, and providing actionable insights for PTIT's management and faculty, ultimately supporting more effective and strategic admission planning.