Development of Automatic Assessment System Based on Machine Learning for Student Learning Evaluation - Tu, Bui Minh; Tu, Nguyen Minh; Nam, Le Hoang (Al-Hijr: Journal of Adulearn World, 2024)
The rapid advancement of machine learning (ML) has significantly impacted educational technologies, particularly in the area of student assessment. Traditional assessment methods often require substantial time and resources, and may not provide immediate or personalized feedback. An automatic assessment system based on machine learning can offer an efficient solution by automating the evaluation process and providing real-time, data-driven insights into student performance. This study explores the development of an automatic assessment system using machine learning algorithms to evaluate student learning and provide personalized feedback in real-time. A mixed-methods approach was used in this research, combining the design and development of the system with quantitative analysis of its effectiveness. The system was tested on 300 students across different academic disciplines, and data was collected from their interactions with the assessment system. Machine learning algorithms, including natural language processing and classification models, were employed to analyze student responses and generate feedback. The results indicate that the machine learning-based system significantly improved the speed and accuracy of student assessments, providing personalized feedback that helped students identify areas for improvement. The system also reduced the administrative burden on educators. This study concludes that machine learning-based automatic assessment systems are a valuable tool for enhancing the learning evaluation process, offering immediate, scalable, and personalized feedback to students.