The Development of a Data Pipeline Process to Support Course-Level Quality Assurance Reporting Based on AUN-QA Criteria: A Case Study of Payap University

Abstract

This applied research aims to develop a data pipeline process to support the preparation of educational quality assurance reports for academic programs, in compliance with AUN-QA (ASEAN University Network-Quality Assurance) standards. The case study is based on Payap University. The process begins by extracting student data from the desired academic year from the database from the Registration and Educational Service Office, Payap University. The extracted data is then reformatted for analysis and stored in a lightweight SQLite database, optimized for faster processing. This data is then used to create educational statistics tables in Microsoft Excel format, which can be used to prepare quality assurance reports for specific academic programs. Python was employed to automate each step of the data pipeline process. Users need only select the academic year and program, and the system will automatically generate five statistical tables: 1) the number of current students, 2) the number of new students, 3) the number of retained students, 4) the number of graduates and students who have left, and 5) the number of resigned students. The system’s user-friendliness and performance were evaluated by one staff member from Payap University's Office of Planning and Organization Development, who oversees quality assurance, along with nine faculty members, each representing a different academic program. The results indicate that the overall user satisfaction is very high. Both the ease of use and system performance were rated "excellent," with average scores of 4.67 and 4.75, respectively. These findings demonstrate that the developed process is highly practical and effective in supporting the generation of quality assurance reports.