Iot-Driven Educational Assessment: Exploring Machine Learning Techniques For Adaptive Evaluation Systems

Abstract

This paper explores the potential of integrating the Internet of Things (IoT) with machine learning (ML) techniques to enhance educational assessments through adaptive evaluation systems. The research focuses on designing an IoT architecture that gathers real-time data from educational environments, which is then analyzed using advanced ML algorithms to tailor the assessment processes to individual learning patterns and needs. The study evaluates the effectiveness of this adaptive system in comparison to traditional assessment methods, highlighting the benefits in terms of increased accuracy, responsiveness, and personalization of learning evaluations. Key challenges such as data privacy, security concerns, and potential biases in ML models are addressed. The findings suggest that IoT-driven adaptive evaluation systems can significantly transform educational assessment methodologies, making them more aligned with 21st-century educational demands.