Hybrid Classification Algorithms for Predicting Student Performance

2021 ◽  
Author(s):  
A.Dinesh Kumar ◽  
R.Pandi Selvam ◽  
V. Palanisamy
Keyword(s):  
Student Performance ◽  
Classification Algorithms ◽  
Hybrid Classification ◽  
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 Prediction of Student Performance using Hybrid Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 6566-6570
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Research Work ◽  
Educational Data Mining ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Data Types ◽  
Data Set ◽  
Hybrid Classification

Data mining technologies allow collection, storage and processing huge amounts of data and carrying a large variety of data types and samples. Predicting academic performance of student is the most successive research in this era. Previous research work researchers are used different classification algorithm to predict the student performance. There is lot of research work to be taken in the field of educational data mining and big data in education to increase the accuracy of the classification algorithm and predict the academic performance of student. In this research work we used hybrid classification algorithm for predicting the performance of students. Two Popular classification algorithms ID3 and J48 were applied on the data set. To make hybrid classification voting technique is applied using weka machine learning tool. In this work we tested how the hybrid algorithm accurately predicts the student data set. To check the predicted result classification accuracy was computed. This hybrid classification algorithm gives accuracy with 62.67%.

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