數據分析在互聯網或商業領域一直以來發揮至關重要的作用，而目前高等教育也開始引入大數據分析來進行招生和學習效果分析。然而，並非所有高等教育機構都擁有足夠的專業程序員來處理和分析大量數據。因此，在本論文中，我們提出了一個機器人流程自動化系統來自動化數據的處理和分析，讓領域專家能夠更專注於探索數據背後的洞見。此外，本論文還將整合不同的數據源以供未來相關研究使用，並將研究成果系統轉化為圖形界面，提高使用便利性。

Data analysis has always played an essential role in the Internet and business areas, and nowadays higher education has also begun introducing big data analysis for enrollment and learning effectiveness analyses. However, not all higher education institutions have enough professional programmers to process and analyze large amounts of data.
Therefore, in this thesis, we propose an RPA (Robotic Process Automation) system to automate the processing and analysis of data, allowing domain experts to focus more on exploring the insights behind the data. In addition, the system will also integrate different data sources for future use in related research, and present research results with a graphical interface to improve the convenience of use.

摘要
目錄

1 Introduction--------------------------------------1
2 Related Work--------------------------------------5
2.1 Data-Driven Decision-Making In Education--------5
2.2 Enrollment Management---------------------------6
2.3 Robotic Process Automation (RPA)----------------6
3 Methods-------------------------------------------8
4 System Architecture------------------------------12
4.1 Data Source Module-----------------------------12
4.1.1 Open Data------------------------------------13
4.1.2 Data Before Student Entrance-----------------14
4.1.3 Data After Student Entrance------------------14
4.2 ETL and RPA Module----------------------------15
4.3 Data Warehouse Module--------------------------15
5 Analysis Module----------------------------------17
5.1 Competitor Analysis----------------------------17
5.1.1 Mobility of Students Among the Universities--17
5.1.2 Mobility of Students Among the Departments---19
5.2 Correlation Analysis---------------------------19
5.2.1 Web Interface--------------------------------21
6 Conclusions and Future Work----------------------23
Bibliography---------------------------------------25

[1] Santiago Aguirre and Alejandro Rodriguez. Automation of a Business Process Using Robotic Process Automation (RPA): A Case Study. In Applied Computer Sciences in Engineering, pages 65–71, 2017.
[2] Kanadpriya Basu, Treena Basu, Ron Buckmire, and Nishu Lal. Predictive Models of Student College Commitment Decisions Using Machine Learning. Data, 4(2), 2019.
[3] Lin Chang. Applying Data Mining to Predict College Admissions Yield: A Case Study. New Directions for Institutional Research, 2006(131):53–68, 2006.
[4] Hanan Abdullah Mengash. Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems. IEEE Access,8:55462–55470, 2020.
[5] Maxim A. Nazarenko and Tatiana V. Khronusova. Big Data in Modern Higher Education. Benefits and criticism. In International Conference on Quality Management, Transport and Information Security, Information Technologies” (IT&QM&IS), pages 676–679, 2017.
[6] Cindy Louise Poortman and Kim Schildkamp. Solving Student Achievement Problems with a Data Use Intervention for Teachers. Teaching and Teacher Education, 60:425–433, 2016.
[7] Ruangsak Trakunphutthirak, Yen Cheung, and Vincent C. S. Lee. A Study of Educational Data Mining: Evidence from a Thai University. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1):734–741, 2019.
[8] Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(1):97–107, 2014.