Analysis of Study Visa Applicants’ Survey Responses Using Data Modeling: A Case Study of Educational Immigration Agencies

Background and Objective: Academic Application Service Management Systems (AASMSs) support educational application processes but may face challenges related to infrastructure, user experience, interface design, and data management. This study aims to identify weaknesses in AASMSs based on applicant feedback and system evaluation and to develop data modeling-based approaches for improving service efficiency and user experience.
Methods: This mixed-methods study was conducted in Tehran in March 2023. Application-service-related websites of 80 international universities were evaluated, from which 30 universities were selected. A performance index based on global ranking, user return rate, and page views was calculated, and 10 universities were selected for in-depth analysis. Survey data were collected from 30 study visa applicants using a researcher-developed questionnaire. Data modeling was conducted using Entity–Relationship (ER) diagrams, while quantitative data were analyzed using SPSS version 25 and the chi-square test.
Results: The average user satisfaction with the evaluated systems was 79.95%. The main challenges identified were insufficient guidance, difficulties in document submission and verification, limitations in online support tools, data security concerns, and unfriendly interfaces. In addition, 57.89% of respondents rated chatbot-based support as good or excellent. Data modeling identified opportunities for automated data validation, intelligent document processing, real-time feedback, and database integration to address identified bottlenecks and improve system performance.
Conclusion: Data modeling provides a structured approach for identifying weaknesses in AASMSs and developing evidence-based improvement strategies. The findings highlight the potential value of interactive guidance, intelligent document review, and centralized data structures in improving service quality, applicant satisfaction, and the efficiency of academic application processes.

Education

1. Khamisu MS, Paluri RA, Sonwaney V. Analysis of the past, present and the future of international student mobility: a retrospective review. South Asian J Bus Manag Cases. 2024;13(1). doi:10.1177/22779779241232187. Available from: https://doi.org/10.1177/22779779241232187 [Google Scholar] [Crossref]

2. Albien AJ, Mashatola NJ. A systematic review and conceptual model of international student mobility decision-making. Soc Incl. 2021;9(1):288-298. doi:10.17645/si.v9i1.3769. Available from: https://doi.org/10.17645/si.v9i1.3769 [Google Scholar] [Crossref]

3. Oldac YI. International student mobility: the need for a more agential approach. J Int Stud. 2023;13(3):i-viii. doi:10.32674/jis.v13i3.6098. Available from: https://ojed.org/jis/article/view/6098 [Google Scholar] [Crossref]

4. Romero C, Ventura S. Educational data mining and learning analytics: An updated survey. WIREs Data Min Knowl Discov. 2020;10(3):e1355. doi:10.1002/widm.1355. Available from: https://doi.org/10.1002/widm.1355 [Google Scholar] [Crossref]

5. Chankseliani M, Kwak J, Hanley N, Akkad A, Crisostomo M, Wang Z. International student mobility and poverty reduction: A qualitative study of the mechanisms of systemic change. World Dev. 2025;195:107116. doi:10.1016/j.worlddev.2025.107116. Available from: https://doi.org/10.1016/j.worlddev.2025.107116 [Google Scholar] [Crossref]

6. Gérard E, Lebeau Y. Trajectories within international academic mobility: A renewed perspective on the dynamics and hierarchies of the global higher education field. Int J Educ Dev. 2023;100:102780. doi:10.1016/j.ijedudev.2023.102780. Available from: https://doi.org/10.1016/j.ijedudev.2023.102780 [Google Scholar] [Crossref]

7. Feng S, Horta H. Brokers of international student mobility: The roles and processes of education agents in China. Eur J Educ. 2021;56(4):590-604. doi:10.1111/ejed.12442. Available from: https://onlinelibrary.wiley.com/doi/10.1111/ejed.12442 [Google Scholar] [Crossref]

8. Nikula PT, Kivistö J. Education agent standards in Australia and New Zealand: government's role in agent-based international student recruitment. Stud High Educ. 2022;47(4):831-846. doi:10.1080/03075079.2020.1811219. Available from: https://doi.org/10.1080/03075079.2020.1811219 [Google Scholar] [Crossref]

9. Stojanov A, Daniel BK. A decade of research into the application of big data and analytics in higher education: A systematic review of the literature. Educ Inf Technol. 2024;29(5):5807-5831. doi:10.1007/s10639-023-12033-8. Available from: https://doi.org/10.1007/s10639-023-12033-8 [Google Scholar] [Crossref]

10. Barbeiro L, Gomes A, Correia FB, Bernardino J. A review of educational data mining trends. Procedia Comput Sci. 2024;237:88-95. doi:10.1016/j.procs.2024.05.083. Available from: https://doi.org/10.1016/j.procs.2024.05.083 [Google Scholar] [Crossref]

11. Vera H, Guo R, Holanda M, Huacarpuma RC, et al. Data Modeling and NoSQL Databases: A Systematic Mapping Review. ACM Comput Surv. 2021;54(6):1-26. doi:10.1145/3457608. Available from: https://doi.org/10.1145/3457608 [Google Scholar] [Crossref]

12. Aydın AA. An In-depth Examination of Logical Data Models Utilized in Data Storage Systems to Facilitate Data Modeling. Gazi Univ J Sci. 2025;38(2):706-729. doi:10.35378/gujs.1467890. Available from: https://doi.org/10.35378/gujs.1467890 [Google Scholar] [Crossref]

13. Luo Y, Latukha M, Panibratov A. International student mobility: A systematic review and research agenda. Int J Consum Stud. 2023;47(3):852-887. doi:10.1111/ijcs.12911. Available from: https://doi.org/10.1111/ijcs.12911 [Google Scholar] [Crossref]

14. Siemens G. Learning analytics: The emergence of a discipline. Am Behav Sci. 2013;57(10):1380-1400. doi:10.1177/0002764213498851. Available from: https://doi.org/10.1177/0002764213498851 [Google Scholar] [Crossref]

15. Pineda-Hernandez J, Bernal GL, Abadía LK, Arango S, De Witte K. Can information change preferences for higher education? Evidence from a randomized controlled trial. Int J Educ Res. 2024;127:102417. doi:10.1016/j.ijer.2024.102417. Available from: https://doi.org/10.1016/j.ijer.2024.102417 [Google Scholar] [Crossref]

16. Eusafzai HAK. Educational capital and international mobility: A Bourdieusian inquiry into choosing peripheral higher education destination. Soc Sci Humanit Open. 2024;10:100916. doi:10.1016/j.ssaho.2024.100916. Available from: https://doi.org/10.1016/j.ssaho.2024.100916 [Google Scholar] [Crossref]

17. Batista C, Costa DM, Freitas P, Lima G, Reis AB. What matters for the decision to study abroad? A lab-in-the-field experiment in Cape Verde. J Dev Econ. 2025;173:103401. doi:10.1016/j.jdeveco.2024.103401. Available from: https://doi.org/10.1016/j.jdeveco.2024.103401 [Google Scholar] [Crossref]

18. Dos Santos LM, Lo HF, Kwee CTT. Australia as the destination for study abroad: International students’ motivations and return intentions. Heliyon. 2024;10(23):e39741. doi:10.1016/j.heliyon.2024.e39741. Available from: https://doi.org/10.1016/j.heliyon.2024.e39741 [Google Scholar] [Crossref]

19. Abd Aziz NA, Ahmed S, Haque R, Qazi SZ, Senathirajah ARBS. Deciphering International Students' Choices: Push-Pull Dynamics and Necessary Condition Analysis in Malaysian University Selection. Int J Knowl Manag. 2025;21(1):1-34. doi:10.4018/IJKM.372675. Available from: https://doi.org/10.4018/IJKM.372675 [Google Scholar] [Crossref]

20. Inouye K, Lee S, Oldac YI. A systematic review of student agency in international higher education. High Educ. 2023;86(4):891–911. doi:10.1007/s10734-022-00952-3. Available from: https://doi.org/10.1007/s10734-022-00952-3 [Google Scholar] [Crossref]

21. Masri K, Parker D, Gemino A. Using iconic graphics in entity-relationship diagrams: The impact on understanding. Journal of Database Management (JDM). 2008 Jul 1;19(3):22-41. DOI: 10.4018/jdm.2008070102 [Google Scholar] [Crossref]

22. Pahune S, Akhtar Z, Mandapati V, Siddique K. The importance of AI data governance in large language models. Big Data and Cognitive Computing. 2025;9(6):147. https://doi.org/10.3390/bdcc9060147 [Google Scholar] [Crossref]

23. Kaber DB, Onal E, Endsley MR. Design of automation for telerobots and the effect on performance, operator situation awareness, and subjective workload. Human factors and ergonomics in manufacturing & service industries. 2000;10(4):409-30. https://doi.org/10.1002/1520-6564 [Google Scholar] [Crossref]

24. Jagatheesaperumal SK, Rahouti M, Ahmad K, Al-Fuqaha A, Guizani M. The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions. IEEE internet of things journal. 2021;9(15):12861-85. DOI: 10.1109/JIOT.2021.3139827 [Google Scholar] [Crossref]

25. Lotfi Z, Mukhtar M, Sahran S, Zadeh AT. Information sharing in supply chain management. Procedia Technology. 2013;11:298-304. https://doi.org/10.1016/j.protcy.2013.12.194 [Google Scholar] [Crossref]

26. Clarke J, Dede C, Ketelhut DJ, Nelson B. A design-based research strategy to promote scalability for educational innovations. Educational Technology. 2006;46(3):27-36. [Google Scholar] [Crossref]

• A Data Mining Model for Clustering Food Consumption Patterns
• Data Mining Meets Blockchain: A Systematic Review of Techniques, Challenges, and Emerging Applications