Proctoring System with Robot Using Deep Learning Techniques
Keywords:
Deep Learning, IOT, ML, AI, NLP, MongoBD, TTS.Abstract
A manual proctoring system is crucial in educational institutions as it oversees exams and maintains accurate records of student’s academic performance. This system relies on human oversight, with administrative personnel meticulously recording each student's academic achievements, including grades and growth. The accuracy and dependability of these records are ensured through manual input and verification procedures, reflecting the institute's commitment to academic integrity. These records are also crucial for parents, teachers, and students, enabling informed decisions and personalized academic assistance. Despite the advent of digital technologies, the individualized care and careful monitoring of manual records maintain the reliability and integrity of student academic performance in all educational settings. The manual proctoring system faces challenges such as high human resource costs, inconsistent exam rules, time-consuming data entry, and limited scalability. It also affects efficiency and timely information availability for large student bodies. Additionally, manual data entry can lead to errors in student records, affecting reliability and openness. The system's interpretation of exam rules may differ among proctors, affecting the overall efficiency and accessibility of the system. Robotic proctoring systems in educational institutions offer numerous benefits over manual methods. They reduce labor-intensive tasks, increase test supervision efficiency, and manage large data sets and student demographics. This scalability reduces administrative hassles and maximizes resource allocation. However, implementation requires careful consideration of technology readiness, initial investment, and staff training. Despite these challenges, robot proctoring is a significant step towards modernizing exam monitoring.
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