EduComply: A progress tracking system for compliance in teaching-learning deliverables using decision support system
This study presents the development of EduComply, a progress tracking system designed to support compliance in teaching-learning deliverables using a Decision Support System (DSS). The system aims to automate and improve the submission, monitoring, and review of key teaching-learning deliverables, including syllabus, self-paced learning modules (SLMs), tables of specifications (TOS), and exams. The study utilized the developmental-descriptive design and Agile methodology; the system was developed through continuous feedback and iteration. It features real-time tracking of syllabus implementation, centralized submission workflows, and automated dashboards that support timely compliance monitoring. The integration of a Decision Support System allows the system to assess three parameters: faculty deliverables, student performance, and activity-based student feedback. This provides administrators with actionable insights that can guide interventions and policy decisions. User evaluations from faculty, administrators, and students showed a high acceptability. The findings show that while the system is highly accepted and effective in technology-oriented colleges, its lower ratings in non-technical colleges indicate the need for improved adaptability and scalability to diverse academic environments. It revealed that the system improved academic compliance while underscoring the need for contextual refinements for broader institutional adoption. The study recommends future enhancements such as integration with existing LMS platforms, grade tracking, and expanded analytics to support long-term implementation and cross-departmental scalability.
Authors
- Francis F. Balahadia (ORCID: https://orcid.org/0000-0001-6666-4975)
- Glaizess Salisi
Publication Details
- Journal
- International Journal of Science Technology Engineering and Mathematics
- Published
- 2025-09-04
- DOI
- https://doi.org/10.53378/ijstem.353240
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00