Department of CSE (Artificial Intelligence & Machine Learning), ACE Engineering College, Hyderabad, Telangana, India
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 2547–2554
Received on 19 February 2024; revised on 28 March 2024; accepted on 31 March 2024
The problem of automated student evaluation and analysis is a widespread issue in the education sector, where the manual evaluation of students' performance and progress can be time-consuming, prone to errors, and lacking in efficiency. The current evaluation methods do not provide real-time insights and feedback to students, leading to a lack of engagement and motivation. Many Methodologies have been developed for estimating and analyzing the students' performance. One such methodology developed using: Machine learning, educational data mining, Predicting achievement. This methodology has no concern for the aspects like non-academic activities, personality traits of students and other technical and non-technical skills. The goal of automated student evaluation and analysis is to develop a system that can accurately and efficiently evaluate and analyze students' performance and provide meaningful feedback in real-time. The system should be able to analyze large amounts of data from various sources, exams, and provide detailed insights into the strengths and weaknesses of individual students. The system should also provide teachers with the tools to track student progress over time, identify areas for improvement, and provide personalized feedback to each student. The challenge of creating such a system lies in accurately analyzing and interpreting large amounts of data, identifying patterns and trends, and providing meaningful insights and feedback to students and teachers in real-time. It requires the integration of advanced artificial intelligence and machine learning techniques, as well as user-friendly interfaces and data visualization tools.
Automated Student Evaluation; Performance Analysis; Real-Time Feedback; Machine Learning; Educational Data Mining; Predictive Achievement; Non-Academic Activities; Personality Traits; Technical Skills.
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Chitoor Venkat Ajay Kumar, Adithya Krishna Eemani, Gouri Charan Kalluri and Gunadeep Rudra. A survey on automated student evaluation and analysis using machine learning. World Journal of Advanced Research and Reviews, 2024, 21(3), 2547-2554. Article DOI: https://doi.org/10.30574/wjarr.2024.21.3.0901