Adaptive Cognitive Learning in Vocational Education: Integrating Deep Learning for Fiber optic Network Competencies - Sa'adah, Nurul Laili; Putra, Muhammad Trio Maulana; Anggana, Syeh Umar; Masykuri, Nuri Muhammadin (EDUTIC, 2025)
The rapid advancement of digital technologies, particularly Deep Learning, presents opportunities to enhance adaptive cognitive learning in vocational education. This study investigates the integration of Deep Learning into the teaching of fiber optic network competencies in the Computer and Network Engineering (TJKT) program at vocational schools. Using a Systematic Literature Review (SLR) based on the PRISMA protocol, relevant studies published between 2020 and 2025 were retrieved from Scopus, IEEE Xplore, and Google Scholar. Findings reveal that Deep Learning enables real-time modeling of student learning patterns, supports personalized content delivery via learning management systems, and facilitates simulation and troubleshooting in fiber optic training. However, challenges include limited training data, inadequate computing infrastructure, and insufficient teacher readiness. The study concludes that implementing Deep Learning can significantly improve practical learning effectiveness, provided that infrastructure and educator competencies are strengthened