Design and User Analysis of a Learning Management System: Student Competency-Based Learning

The rapid evolution of digital technology in education has highlighted the need for robust platforms that support student competency-based learning, wherein each learner progresses at an individualized pace and demonstrates mastery of specific competencies. Building on evidence that personalized instruction improves engagement and learning outcomes, this study aims to develop a Learning Management System (LMS) capable of enhancing both formative and summative assessments. The primary objectives are to facilitate the uploading of learning materials, manage user roles (teachers, students, administrators), and provide flexible assignment distribution—all while promoting self-directed, sustainable learning. An Agile methodology was adopted to ensure iterative development and close collaboration with stakeholders, allowing for quick adaptations to evolving requirements. System architecture was designed using UML, focusing on role-based workflows and clear user interfaces. Throughout the process, regular sprints were conducted, incorporating continuous testing and feedback loops to refine functionality. The LMS was then evaluated through usability testing using the System Usability Scale (SUS). Findings from 80 student participants yielded an average SUS score of 81.75, which falls into the “very good” category, suggesting high user acceptance and ease of use. These results affirm the system’s effectiveness in supporting competency-based learning, as students can monitor individual progress in real-time and receive timely feedback from teachers. Moreover, teachers benefit from streamlined assessment processes, enabling them to devote more attention to pedagogical improvements. Although this research was conducted in a single school environment and over a relatively short period, the encouraging results indicate strong potential for broader implementation. Future development may integrate features such as learning analytics, gamification, and personalized content recommendations, thereby further enhancing adaptive learning experiences across diverse educational contexts.