International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
STUDENT PERFORMANCE PREDICTION SYSTEM Shruti Lokhande1, Anjali Shendge2, Asmita Lahane3, Poonam Pawar4, PROF. Manisha Kapse5 1,2,3,4 (Students, Department of Computer Engineering), S.Y.P Shreeyash College Of Engineering And Technology
(Polytechnic), Chh. Sambhajinagar , India
5(Professor, Dept. of Computer Engineering), S.Y.P Shreeyash College Of Engineering And Technology
(Polytechnic), Chh.Sambhajinagar , India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Student performance prediction is an important
teachers make informed decisions and enhance the quality of education overall.
area in the field of education and data analysis. This paper presents a Student Performance Prediction System that uses machine learning techniques to predict students’ academic results based on various factors such as attendance, previous marks, study time, participation, and other personal or academic details. The main goal of the system is to identify students who may need extra support at an early stage. The proposed system collects and processes student data, applies data preprocessing techniques, and trains different machine learning models to predict performance outcomes. The model with the best accuracy is selected for final prediction. The system helps teachers and educational institutions make informed decisions to improve student success rates. Key Words: Student Performance Prediction, Machine Learning, Educational Data Mining, Academic Analysis, Predictive Modelling, Learning Analytics
1. INTRODUCTION Education plays a very important role in personal and professional development. Academic performance, especially of students, is considered a supportive key indicator reflecting their progress of learning and future success. However, many students undergo a lot of difficulties in studying due to different academic, personal, and social factors. The identification of such students at an early stage would assist teachers and institutions in providing timely support and guidance.
Fig 1: System Architecture 1. Educational System Layer At the top level, the Educational System forms the operational environment. It includes: Traditional classroom learning E-learning or online learning platforms Hybrid learning environments
In this modern age, with the growth of technology and digital learning systems, a lot of data about students can be obtained. They include attendance, previous examination marks, assignment scores, study hours, participation in activities, and every relevant factor. Such data can then be used to make more accurate predictions concerning student performance by utilizing data mining and machine learning analysis.
The educational system acts as the primary source of data and interaction between teachers and students. 1) Roles in the System
These techniques analyze the historical data and identify patterns in the Student Performance Prediction System that influence academic outcomes. It will classify students in terms of their expected performances and highlight those students who may need special attention. This will help
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Teachers: Responsible for planning, designing, and maintaining course structure and content. Students: Engage with the system by communicating, participating, and using academic resources.
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