scholarly journals Predicting Student Performance for Early Intervention using Classification Algorithms in Machine Learning

2021 ◽  
Vol 9 (36) ◽  
pp. 226-235
Author(s):  
Kalaivani K ◽  
Ulagapriya K ◽  
Saritha A ◽  
Ashutosh Kumar
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Student Performance ◽  
Classification Algorithms ◽  
Predicting Student Performance

scholarly journals Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques

2021 ◽  
Vol 10 (3) ◽  
pp. 38-49
Author(s):  
Deepti Aggarwal ◽  
Sonu Mittal ◽  
Vikram Bali
Keyword(s):  
Student Performance ◽  
Technical College ◽  
Prediction Models ◽  
Preventive Measures ◽  
Early Stage ◽  
Primary Data ◽  
Classification Algorithms ◽  
Data Set ◽  
Learning Techniques ◽  
Predicting Student Performance

The academic institutions are focusing more on improving the performance of students using various data mining techniques. Prediction models are designed to predict the performance of students at a very early stage so that preventive measures can be taken beforehand. Various parameters (academic as well as non-academic) are considered to predict the student performance using different classifiers. Normally, academic parameters are given more weightage in predicting the academic performance of a student. This paper compares the two models: one built using academic parameters only and another using both academic and non-academic (demographic) parameters. The primary data set of students has been taken from a technical college in India, which consists of data of 6,807 students containing attributes. Synthetic minority oversampling technique filter is applied to deal with the skewed data set. The models are built using eight classification algorithms that are then compared to find the parameters that help to give the most appropriate model to classify a student based on his performance.

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

scholarly journals Machine Learning Technique Analysis and Applications for Predicting Student Performance

2019 ◽  
Vol 9 (1) ◽  
pp. 2133-2136
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Student Performance ◽  
Learning Algorithm ◽  
Decision Support Tool ◽  
Research Field ◽  
Machine Learning Algorithm ◽  
Support Tool ◽  
Predicting Student Performance ◽  
User Friendly

Machine Learning is an emerging research field concerned with developing methods to answer uncommon problems. There are many problems that can be answered with Machine Learning method, one of them is on educational scope. Many Educators right now cannot identify whether a certain student is on the brink of failing or not. As a result, many college students failed because the educators cannot help them. In this paper, we present our user-friendly decision support tool made from Machine Learning algorithm and to answer the problem we focus, which is to prevent college student from failing by providing educational agents necessary information and predictions. Our objective is to know which machine learning algorithm that can be used to predict the student’s performance and to create a decision support tool that can be used by educational agents so that educational agents can prevent student from failing the course.

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