
Prediction of students’ performance in education system based on artificial intelligence
Author(s) -
Pooja Pathak,
Nazia Farheen,
Avinash Dubey
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1116/1/012132
Subject(s) - decision tree , naive bayes classifier , computer science , machine learning , artificial intelligence , artificial neural network , recall , logistic regression , educational data mining , precision and recall , tree (set theory) , measure (data warehouse) , data mining , support vector machine , psychology , mathematics , cognitive psychology , mathematical analysis
The main objective of any educational system is to provide the best knowledge to students. To achieve this goal this is important to identify the weak students who need more support and take correct decisions to improve their performance. In this research for predicting the students’ performance, four techniques of machine learning are used. For this technique, we take the data from computer science students of GLA University, Mathura, U.P. INDIA. These machine learning techniques include various processes such as Artificial Neural Networks (ANN), Naive Bayes (NB), Logistic Regression, and Decision Tree. In this model, we put more efforts to know the time attended by the students on the internet for learning and social media. Also, various measurements have been done such as precision, F measure, recall, and classification errors. We used the dataset for building a model depending upon the survey that given to the all-computer science students and grade copy of students. The decision tree identified four main attributes that influence the performance of students a lot. This helps us to achieve an accurate prediction of around 98%.