Data-Driven Analysis of Computer-Based Testing to Advance Machinist Performance

• Main Authors: Irwansyah, ., Helda, Yudiastuti, Misinem, ., Andre, Hardoni
• Format: Article
• Language: English
• Published: INTI International University 2024
• Subjects: Q Science (General); QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://eprints.intimal.edu.my/2023/1/jods2024_42.pdf; http://eprints.intimal.edu.my/2023/; http://ipublishing.intimal.edu.my/jods.html

The rapid advancement of technology has transformed the education sector, offerings new avenues for data-driven teaching and learning innovations. This study investigates the integration of Augmented Reality (AR) technology in developing an interactive learning media application for scout password recognition, with a focus on analyzing learner interaction data to evaluate its effectiveness. The application utilizes marker-based tracking to overlay digital content in the real world, creating an immersive environment that enhances comprehension and retention. The study employs the Prototype Method to ensure user-centric design, supported by stakeholder feedback throughout iterative development. Unified Modeling Language (UML) tools, such as Use Case and Activity Diagrams, were utilized to model system functionality. Key features of the application include interactive 3D models, gamification elements, and progress tracking, with user interaction data analyzed to assess engagement and learning outcomes. System functionality was evaluated using the Blackbox testing method, and user performance data was analyzed to identify patterns in engagement, motivation, and understanding of scout passwords. Results reveal a significant improvement in learner outcomes compared to traditional teaching methods, with data analysis highlighting areas of particular effectiveness, such as the use of gamification to sustain learner interest. This research not only underscores the potential of AR in transforming niche educational contexts but also emphasizes the importance of analyzing interaction and performance data to refine educational tools. Future development recommendations include incorporating AI-powered personalized learning features and expanding the application to cover additional scouting skills, paving the way for broader adoption of AR technology in education.