Open Access
Subscription Access
Open Access
Subscription Access
Predicting Student Performance Using Data Mining
Subscribe/Renew Journal
The objective of the paper is to analyze the performance of a student by evaluating EQ and IQ level and also to check the correlation between EQ IQ and performance. Present system predicts the performance of student by comparing their EQ and IQ with some tests referred by the psychology department. Our study aims to establish and monitor the performance using EQ and IQ achievement among the undergraduate students by data mining procedures. Initially prepared the questionnaire of EQ and IQ. The data were collected from 150 students. Using WEKA tool, the data given as input. Using different classification algorithms predicted the individual performance of student by data mining concepts. And also compare the efficiency of classification algorithms and find the efficient one among them. EQ IQ is found to have a significant relation with the performance of students. Logistics function and random tree were used to explore the performance of student semester by semester. By means of applying these methods, the performance by graduation is predicted and accuracy is evaluated.
Keywords
Data Mining, Emotional Quotient, Intelligent Quotient, Logistic Function, WEKA Tool.
Subscription
Login to verify subscription
User
Font Size
Information
- M. K. Panth, V. Sahu, and M. Gupta, “A comparative study of emotional intelligence and intelligence quotient between introvert and extrovert personality,” International Journal of Research in Humanities, Arts and Literature, vol. 3, no. 5, pp. 41-54, May 2015.
- M. F. Noordin, and Z. A. Karim, “The effect of IQ vs. EQ on knowledge management and innovation.” Information and Communication Technology for The Muslim World (ICT4M), 2014 The 5th International Conference on, IEEE, 2014.
- N. Saibani, M. I. Sabtu, Z. Harun, W. M. F. Wan Mahmood, N. I. Muhamad, D. A. Wahab, and J. Sahari, “Comparison of emotional intelligence scores among engineering students of an academic programme,” Journal of Engineering Science and Technology, vol. 10, no. 2 on UKM Teaching and Learning Congress 2013, pp. 41-51, 2015.
- T. Mishra, D. Kumar, and S. Gupta, “Students’ employability prediction model through data mining,” International Journal of Applied Engineering Research, vol. 11, no. 4, pp. 2275-2282, 2016.
- B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, W. F. Punch, “Predicting student performance: An application of data mining methods with an educational web-based system,” Frontiers in education, 2003. FIE 2003 33rd Annual, vol. 1. IEEE, 2003.
- U. H. Gondal, and T. Husain. “A comparative study of intelligence quotient and emotional intelligence: Effect on employees’ performance,” Asian Journal of Business management, vol. 5, no. 1, pp. 153-162, 2013.
- Mayilvaganan, M., and D. Kalpanadevi, “Comparison of classification techniques for predicting the performance of students’ academic environment,” Communication and Network Technologies (ICCNT), 2014 International Conference on, IEEE, 2014.
- M. Mayilvaganan, and D. Kalpanadevi, “Comparison of classification techniques for predicting the cognitive skill of students in education environment,” Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on, IEEE, 2014.
- K. P. Shaleena, and S. Paul, “Data mining techniques for predicting student performance,” Engineering and Technology (ICETECH), 2015 IEEE International Conference on, IEEE, 2015.
- U. B. Mat, N. Buniyamin, P. M. Arsad, and R. A. Kassim, “An overview of using academic analytics to predict and improve students’ achievement: A proposed proactive intelligent intervention,” Engineering Education (ICEED), IEEE 5th Conference on, IEEE, 2013.
- S. K. Mohamad, and Z. Tasir, “Educational data mining: A review,” Procedia-Social and Behavioral Sciences, vol. 97, pp. 320-324, 2013.
- S. Mouneshachari, M. B. Sanjay Pande, and T. S. Satyanarayana Rao, “EQ and IQ based classification of intelligent index (S-Quotient) using K-means,” Advanced Computing (IACC), 2016 IEEE 6th International Conference on, IEEE, 2016.
- D. Goleman, “Emotional intelligence. why it can matter more than IQ,” Learning, vol. 24, no. 6, pp. 49-50, 1996.
- J. D. Mayer, D. R. Caruso, and P. Salovey, “Emotional intelligence meets traditional standards for an intelligence,” Intelligence, vol. 27, no. 4, pp. 267-298, 1999.
- J. Mayer, P. Salovey, and D. Caruso, “Emotional intelligence,” In R. J. Sternberg (ed.), Handbook of Intelligence, pp. 396-421, 2000.
Abstract Views: 376
PDF Views: 6