• K. Sagar
• L. Bharadwaz
• M. Surya Raga Vendhra
• E. Benitha Sowmiya

In recent years, the integration of machine learning techniques into educational assessment has garnered significant attention due to its potential to provide valuable insights into student performance and academic achievement. This study aims to evaluate student academic achievement in an engineering program through the application of machine learning algorithms. The research utilizes historical academic data, including grades, exam scores, and other relevant metrics, to train and test various machine learning models. These models are then employed to predict student performance and identify key factors influencing academic success within the engineering curriculum. Through rigorous analysis and comparison of different machine learning approaches, this study seeks to provide valuable insights into enhancing educational practices and facilitating personalized learning strategies within engineering programs. The findings of this research have the potential to inform educators, administrators, and policymakers in designing more effective interventions and support mechanisms to enhance student success in engineering education.

Student Level, Behaviour, Monitoring, Online Class, Artificial Intelligence

"Evaluating Student Academic Achievement in an Engineering Program using Machine Learning techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e50-e53, April-2024, Available :http://www.jetir.org/papers/JETIR2404405.pdf

"Evaluating Student Academic Achievement in an Engineering Program using Machine Learning techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe50-e53, April-2024, Available at : http://www.jetir.org/papers/JETIR2404405.pdf