The week's pick
Reseach Article
An Efficient Data Mining Approach to Improve Students’ Employability Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 47 |
| Year of Publication: 2019 |
| Authors: Nancy Kansal, Vineet Kansal |
10.5120/ijca2019919372
|
Nancy Kansal, Vineet Kansal . An Efficient Data Mining Approach to Improve Students’ Employability Prediction. International Journal of Computer Applications. 178, 47 ( Sep 2019), 29-35. DOI=10.5120/ijca2019919372
Abstract
Students’ Employability Prediction is a major concern for the institutions offering higher education. A method for early prediction of employability of the students is always desirable to take timely action. In this paper, an efficient data mining approach is proposed to improve students’ employability prediction. For Students’ Employability Prediction, there are some steps which include data acquisition, data pre-processing, feature subset selection and decision and ranking. For feature subset selection, Chi-Square, Gini index, Information Gain and Correlation Coefficient methods are used and for best feature selection Crow Search based Feature Selection Algorithm is used. Hybridized Hidden Markov Model and Support Vector Machine (HMM-SVM) is used for the prediction of employability. The experimental results are carried out and compared with some existing methods which includes the classification based on Support Vector Machine (SVM), Hidden Markov Model (HMM), k-Nearest Neighbour (kNN) and Artificial Neural Network (ANN). When comparing with the existing methods, the experimental outcomes showed that 93.4% accuracy was obtained by using HMM-SVM classifier.
References
- Peña-Ayala, Alejandro. "Educational data mining: A survey and a data mining-based analysis of recent works." Expert systems with applications 41.4 (2014): 1432-1462.
- Campagni, Renza, et al. "Data mining models for student careers." Expert Systems with Applications 42.13 (2015): 5508-5521.
- Ahmed, Shibbir, Rajshakhar Paul, and Abu Sayed Md Latiful Hoque. "Knowledge discovery from academic data using association rule mining." Computer and Information Technology (ICCIT), 2014 17th International Conference on. IEEE, 2014.
- Liao, Shu-Hsien, Pei-Hui Chu, and Pei-Yuan Hsiao. "Data mining techniques and applications–A decade review from 2000 to 2011." Expert systems with applications 39.12 (2012): 11303-11311.
- Dutt, Ashish, Maizatul Akmar Ismail, and Tutut Herawan. "A systematic review on educational data mining." IEEE Access 5 (2017): 15991-16005.
- Shahiri, Amirah Mohamed, and Wahidah Husain. "A review on predicting student's performance using data mining techniques." Procedia Computer Science 72 (2015): 414-422.
- Asif, Raheela, et al. "Analyzing undergraduate students' performance using educational data mining." Computers & Education 113 (2017): 177-194.
- Kotsiantis, Sotiris B., C. J. Pierrakeas, and Panayiotis E. Pintelas. "Preventing student dropout in distance learning using machine learning techniques." International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer, Berlin, Heidelberg, 2003.
- Yasmin, Dr. "Application of the classification tree model in predicting learner dropout behaviour in open and distance learning." Distance Education 34.2 (2013): 218-231.
- Burgos, Concepción, et al. "Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout." Computers & Electrical Engineering(2017).
- Jackson, Denise. "Business graduate employability–where are we going wrong?." Higher Education Research & Development 32.5 (2013): 776-790.
- Şen, Baha, Emine Uçar, and Dursun Delen. "Predicting and analyzing secondary education placement-test scores: A data mining approach." Expert Systems with Applications 39.10 (2012): 9468-9476.
- Geng, Ruibin, Indranil Bose, and Xi Chen. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining." European Journal of Operational Research 241.1 (2015): 236-247.
- Bunkar, Kamal, et al. "Data mining: Prediction for performance improvement of graduate students using classification." Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on. IEEE, 2012.
- Keramati, Abbas, et al. "Improved churn prediction in telecommunication industry using data mining techniques." Applied Soft Computing 24 (2014): 994-1012.
- Macfadyen, Leah P., and Shane Dawson. "Mining LMS data to develop an “early warning system” for educators: A proof of concept." Computers & education 54.2 (2010): 588-599.
- Xing, Wanli, et al. "Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory." Computers in Human Behavior 47 (2015): 168-181.
- Thai-Nghe, Nguyen, et al. "Recommender system for predicting student performance." Procedia Computer Science1.2 (2010): 2811-2819.
- Mishra, Tripti, Dharminder Kumar, and Sangeeta Gupta. "Students' Employability Prediction Model through Data Mining." International Journal of Applied Engineering Research 11, no. 4 (2016): 2275-2282.
- Y. Bharambe, N. Mored, M. Mulchandani, R. Shankarmani and S. G. Shinde, "Assessing employability of students using data mining techniques," Advances in Computing, Communications and Informatics, Udupi, 2017, pp. 2110-2114.
- Thakar, Pooja, and Anil Mehta. "Role of Secondary Attributes to Boost the Prediction Accuracy of Students Employability Via Data Mining." arXiv preprint arXiv:1708.02940 (2017).
- Jantawan, Bangsuk, and Cheng-Fa Tsai. "The application of data mining to build classification model for predicting graduate employment." arXiv preprint arXiv:1312.7123 (2013).
- Piad, Keno C., Menchita Dumlao, Melvin A. Ballera, and Shaneth C. Ambat. "Predicting IT employability using data mining techniques." In Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC), pp. 26-30. IEEE, 2016.
Index Terms
Keywords