Digital Literacy: Comparative Review on Machine Learning Based Performance Assessment of Students
Year: 2024
Type: article
Abstract: The E-learning system paved an opportunity to make drastic changes in the educational system all over the world. Several institutions began to implement online learning to offer internet based courses contrary to the traditional classroom teaching. These online courses tends to provide several potential benefits such as flexibility and opportunities, to discover knowledge of the students. It also offers innovations in learning strategies of the students and resolve several complexities by accessing information from internet. Though e-learning based systems produces certain advantages, they also possess limitations of co-operative learning, active learning and performance mitigations. To address these issues, the present study focused on the different AI based techniques used in the prediction of student’s academic performance. The main objective of the study is to analyze the primary factors that affects the learning through online and analyze the performance using different intelligent approaches. A comparative study of the AI based techniques is performed to analyze the different methods involved in the assessment of academic performance. Further, the present issues and future works of the studies is deliberated to produce optimized analysis systems. This tends to support several researchers to overcome the disputes and provide effective e-learning assessment systems.
DOI: 10.4108/eetiot.6711
Authors: K. Shwetha, S. Shahar Banu
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4404393235