Enhancing Learning Outcomes in VR/AR and Robotics Programming Courses Using Real- Time Adaptive Feedback

Abstract

The educational process of teaching programming skills to students in Virtual Reality (VR), Augmented Reality (AR), and Robotics domains creates major obstacles for engineering programs which teach through projects with many students enrolled. Students face challenges when they receive slow, generic feedback during their code review sessions, which prevents them from learning through multiple attempts and understanding programming concepts better. The educational program of this research uses real-time adaptive feedback, which operates as a teaching tool for AR/VR and robotics programming courses to boost student achievement and enable teachers to instruct more students. The intervention received its first implementation during the academic year 2024–25 when three undergraduate engineering courses at the university started teaching their content to 111 students who were enrolled in these courses. Students receive adaptive feedback through their regular programming work because instructors can provide them feedback which matches their course learning objectives and helps them learn. Researchers used a mixed-method evaluation approach, which combined standard coding rubrics with learning analytics and student perception data to measure academic achievement, student involvement, and their belief in their own abilities. The research findings show that students needed 34.1% less time to debug their programs, while their code quality scores improved by 11.3%, and their confidence and competence levels showed substantial progress. The system enabled instructors to reduce their grading duties by 62 per cent while they kept their teaching standards at the same level. The study demonstrates adaptive feedback systems help engineering education programs which teach programming in emerging technology fields through outcome-based methods that work for large student groups and focus on student needs.