A survey on automated student evaluation and analysis using machine learning

Abstract

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.