This paper is published in Volume-4, Issue-3, 2018
Area
Data Mining
Author
Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal
Org/Univ
Centre for Computers and Communication Technology, South Sikkim, Sikkim, India
Keywords
Data mining, C4.5 Algorithms, Decision tree, WEKA (Waikato Environment for Knowledge Analysis) tool.
Citations
IEEE
Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal. A review on tracking of student performance using decision tree, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal (2018). A review on tracking of student performance using decision tree. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.
MLA
Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal. "A review on tracking of student performance using decision tree." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.
Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal. A review on tracking of student performance using decision tree, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal (2018). A review on tracking of student performance using decision tree. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.
MLA
Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal. "A review on tracking of student performance using decision tree." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.
Abstract
The main objective of this paper is an attempt to use data mining methodologies to study and track the student’s academic performance in the subject, is to help in enhancing the educational institutions by evaluating and classifying student data to study the main attributes that may affect the student performance in the subject. This paper focused on improving student academic performance based on their semester marks, class assignments, and extra curriculum activity. Tracking students’ performance will help the learner to know about their performance and it gives a chance to improve their performance in future. The dataset used for the tracking students ‘academic performance include semester marks, class assignments, extra curriculum activity. This paper is mostly focused on the C4.5 algorithm to track the student performance.
