Journal Article

Predicting Academic Performance of Students Using a Hybrid Data Mining Approach

Francis, Bindhia K, Suvanam Sasidhar Babu

2019

Overview

Data mining offers strong techniques for different sectors involving education. In the education field the research is developing rapidly increasing due to huge number of student’s information which can be used to invent valuable pattern pertaining learning behavior of students. The institutions of education can utilize educational data mining to examine the performance of students which can support the institution in recognizing the student’s performance. In data mining classification is a familiar technique that has been implemented widely to find the performance of students. In this study a new prediction algorithm for evaluating student’s performance in academia has been developed based on both classification and clustering techniques and been ested on a real time basis with student dataset of various academic disciplines of higher educational institutions in Kerala, India. The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in prediction of academic performance of the students.

• Publisher: Springer Nature B.V
• Subject: Academic achievement / Algorithms / Classification / Clustering / Data mining / Education / Higher education institutions / Learning behavior / Performance prediction / Students