Intelligent Forms Automation for Higher Ed: Streamlining Student Onboarding and Administrative Workflows
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P110Keywords:
Intelligent forms automation, Higher education, Student onboarding, Intelligent document processing (IDP), OCR and NLP, Workflow orchestration, Business process management (BPM), Automated validation, Student information systems (SIS)Abstract
Higher education institutions increasingly face pressure to deliver seamless digital experiences while managing large volumes of student and administrative data. However, there is still a high number of critical processes such as admissions, financial aid, course registration and internal approvals that rely on paper-based or semi-digital forms which have to be handled by many people. This paper introduces a smart forms automation system designed to meet higher education, which involves intelligent document processing (IDP), content knowledge based on natural language processing, automated validation, and workflow coordination. Documents and forms received through the portal are scanned using OCR and layout-sensitive parsing, classified, and processed to obtain significant student and administrative data. An anomaly/fraud/data-quality Hybrid validation layer A hybrid validation layer is a combination of policy-rule engines and ML-based data quality checks and fraud/anomaly detectors to allow high-confidence, straight-through processing with exceptional cases being sent to human reviewers. These abilities are combined with a workflow engine which is BPM-based, synchronizing approvals, escalations and updates within student information systems (SIS), ERP, CRM and document repositories. On an experimental mixed institutional and benchmark forms dataset, it is demonstrated that there are strong advances in text extraction precision, document grouping, processing, and workflow finishing rates when contrasted with manual and legacy OCR-based techniques. The paper also talks about implementation issues, integration trends, constraints as well as future research and position intelligent forms automation as a major facilitator of digital transformation of higher education
Downloads
References
[1] Albazar, H. (2020). A new automated forms generation algorithm for online assessment. Journal of Information & Knowledge Management, 19(01), 2040008.
[2] Elhoseny, M., Metawa, N., Darwish, A., & Hassanien, A. E. (2017). Intelligent information system to ensure quality in higher education institutions, towards an automated e-university. International Journal of Computational Intelligence Studies, 6(2-3), 115-149.
[3] Noble, D. F. (2012). Digital diploma mills: The automation of higher education. Aakar Books.
[4] Luckow, A., & Jha, S. (2019). Performance characterization and modeling of serverless and HPC streaming applications. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 5688–5696). IEEE. https://doi.org/10.1109/BigData47090.2019.9006530.
[5] Appalaraju, S., Jasani, B., Urala Kota, B., Xie, Y., & Manmatha, R. (2021). DocFormer: End-to-end transformer for document understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021). arXiv:2106.11539.
[6] Jayoma, J. M., Moyon, E. S., & Morales, E. M. O. (2020, December). OCR based document archiving and indexing using PyTesseract: A record management system for dswd caraga, Philippines. In 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) (pp. 1-6). IEEE.
[7] OCR Pipeline for Document Processing, online. https://softwarecountry.com/company/our-blog/ocr-pipeline-for-document-processing/
[8] Nguyen, T. T. H., Jatowt, A., Coustaty, M., & Doucet, A. (2021). Survey of post-OCR processing approaches. ACM Computing Surveys (CSUR), 54(6), 1-37.
[9] Steenbergen Hu, S., & Cooper, H. (2013). A meta analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347. https://doi.org/10.1037/a0034752.
[10] Coombs, C., Hislop, D., Taneva, S. K., & Barnard, S. (2020). The strategic impacts of intelligent automation for knowledge and service work: An interdisciplinary review. The Journal of Strategic Information Systems, 29(4), Article 101600. https://doi.org/10.1016/j.jsis.2020.101600.
[11] He, Y. (2020, September). Research on text detection and recognition based on OCR recognition technology. In 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) (pp. 132-140). IEEE.
[12] Appalaraju, S., Jasani, B., Urala Kota, B., Xie, Y., & Manmatha, R. (2021). DocFormer: End‑to‑end transformer for document understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021). arXiv:2106.11539.
[13] Tayal, D. K., Vij, S., Malik, G., & Singh, A. (2017). An ocr based automated method for textual analysis of questionnaires. Indian Journal of Computer Science and Engineering (IJCSE).
[14] Steenbergen Hu, S., & Cooper, H. (2013). A meta analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347. https://doi.org/10.1037/a0034752.
[15] Mori, S., & Bunke, H. (1997). Handbook of Character Recognition and Document Image Analysis. World Scientific Publishing Company.
[16] Young, N. T., & Caballero, M. D. (2019). Using machine learning to understand physics graduate school admissions. arXiv. https://arxiv.org/abs/1907.01570
[17] Cutting, G. A., & Cutting-Decelle, A. F. (2021). Intelligent Document Processing--Methods and Tools in the real world. arXiv preprint arXiv:2112.14070.
[18] Chen, H., Wen, Y., Zhu, M., Huang, Y., Xiao, C., Wei, T., & Hahn, A. (2021). From automation system to autonomous system: An architecture perspective. Journal of Marine Science and Engineering, 9(6), 645.
[19] Wu, Q. H., Buse, D. P., Feng, J. Q., Sun, P., & Fitch, J. (2004). E-automation, an architecture for distributed industrial automation systems. International Journal of Automation and Computing, 1(1), 17-25.
[20] Tyagi, A. K., Fernandez, T. F., Mishra, S., & Kumari, S. (2020, December). Intelligent automation systems at the core of industry 4.0. In International conference on intelligent systems design and applications (pp. 1-18). Cham: Springer International Publishing.
[21] Sowa, J. F. (2002). Architectures for intelligent systems. IBM Systems Journal, 41(3), 331-349.
