Development of Interactive Learning Media Based on Deep Learning in the Course Programmable Logic Controller

Nasir, Fitra Rabiul Tsani
Sidik, Darlan
Arfandi, Anas

IJORER : International Journal of Recent Educational Research
Vol. 7 No. 5 (2026): September

This study aims to develop web-based interactive learning media integrated with Deep Learning methods for the Programmable Logic Controller (PLC) course, with a focus on Omron PLC material. The development addresses key challenges in PLC learning, including limited access to practical equipment, the lack of interactive digital media, and the absence of intelligent adaptive learning systems. This study employs a Research and Development (R&D) approach by synthesizing the 4D, ADDIE, and Borg & Gall models into five stages: needs analysis, design, development, evaluation, and dissemination. The main novelty of this study lies in the implementation of a Deep Learning–driven adaptive learning mechanism embedded within a web-based PLC simulation environment, which goes beyond conventional web-based PLC learning systems that typically provide static content and rule-based feedback. Unlike existing approaches, the proposed system utilizes an Artificial Neural Network (ANN) model to analyze student interaction patterns, identify learning difficulties in ladder diagram logic, and generate personalized feedback and adaptive learning pathways in real time. The developed system integrates PLC learning materials, JavaScript-based ladder diagram simulations, and AI-driven adaptive assessment features. Validation results indicate a very high level of feasibility (99% for content and 97% for design). Practicality testing shows scores ranging from 81% to 91%, categorized as very feasible. The effectiveness test reveals a statistically significant improvement in student learning outcomes based on pretest–posttest analysis (p-value 0.000 < 0.05), indicating the system’s effectiveness in enhancing conceptual understanding, ladder diagram skills, and learning motivation. This study contributes to the advancement of intelligent vocational learning systems by introducing an adaptive, data-driven PLC learning environment that bridges the gap between simulation-based learning and artificial intelligence–supported personalization. Therefore, the proposed media is considered highly feasible, practical, and effective for implementation in vocational education.