Abstract
The purpose of higher education organizations is to offer superior education to its students. The proficiency to forecast student’s achievement is valuable in affiliated ways associated with organization education system. Students’ scores which they got in exam, can be used to invent training set for dominate learning algorithms. With the academia attributes of students such as internal marks, lab marks, age etc. it can be easily predict their performance. After getting predicted results, improvement in the performance of the student to engage with desirable assistance to the students has to be processed. Educational Data Mining (EDM) offers such information to educational organization from educational data. EDM provides various methods for prediction of student’s performance, which improve the future results of students. In this paper, by using their attributes such as academic records, age, and achievement etc., EDM is used for predicting the performance about placement of final year students. As a result, higher education organizations will offer superior education to its students.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Molina, M. M., Luna, J. M., Romero, C., & Ventura, S.: Meta-learning approach for automatic parameter tuning: a case of study with educational datasets. In Proceedings of the 5th international conference on educational data mining. pp. 180–183, (2012).
Pardos, Z. A., Wang, Q. Y., & Trivedi, S.: The real world significance of performance prediction. (2012).
Thai-Nghe, N., Horváth, T., & Schmidt-Thieme, L: Factorization models for forecasting student performance. In Proceedings of the 4th international conference on educational data mining. pp. 11–20, (2011).
B. Sen, E. Ucar, D. delen: Predicting and analyzing secondary education placement-test scores. (2012).
Baker, R. S. J. D., Gowda, S. M., & Corbett, A. T.: Automatically detecting a student’s preparation for future learning: Help use is key. In Proceedings of the 4th international conference on educational data mining. pp. 179–188, (2011).
Pardos, Z. A., Gowda, S. M., Baker, R. S. J. D., & Heffernan, N. T.: Ensembling predictions of student post-test scores for an intelligent tutoring system.. In Proceedings of the 3rd international conference on educational data mining pp. 299–300, (2011).
Marquez-Vera, C., Romero, C., & Ventura, S.: Predicting school failure using data mining. In Proceedings of the 4th international conference on educational data mining. pp. 271–275, (2011).
J. Akcapinar, G., Cosgun, E., & Altun, A.: Prediction of perceived disorientation in online learning environment with random forest regression. In Proceedings of the 4th international conference on educational data mining, pp. 259–263, (2011).
Schoor, C., & Bannert, M.: Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behaviour, 28(4), pp. 1321–1331, (2012).
Wang, Y., & Heffernan, N. T.: Leveraging first response time into the knowledge tracing model. In Proceedings of the 5th international conference on educational data mining. pp. 176–179, (2012).
Gowda, S. M., Rowe, J. P., Baker, R. S.J. D., Chi, M., & Koedinger, K. R.:Improving models of slipping, guessing, and moment-by-moment learning with estimates of skill difficulty. In Proceedings of the 4th international conference on educational data mining. pp. 199–208, (2011).
Swarnalatha P., Tripathy B.K.: A Comparative Study of RIFCM with Other Related Algorithms from Their Suitability in Analysis of Satellite Images Using Other Supporting Techniques. Kybernetes, Emerald Publications.vol. 43, No 1, pp. 53–81, (2014).
Tripathy B.K., Rohan Bhargava, Anurag Tripathy, Rajkamal Dhull, Ekta Verma, Swarnalatha P.: Rough Intuitionistic Fuzzy C-Means Algorithm and a Comparative Analysis. In: Proceedings of ACM Compute-2013. pp. 21–22, (2013).
Acknowledgments
The author thankfully acknowledges the Apurva Mohan Gupta and others for their support for the completion of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Ramanathan, L., Geetha, A., Khalid, M., Swarnalatha, P. (2016). Apply of Sum of Difference Method to Predict Placement of Students’ Using Educational Data Mining. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 433. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2755-7_39
Download citation
DOI: https://doi.org/10.1007/978-81-322-2755-7_39
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2753-3
Online ISBN: 978-81-322-2755-7
eBook Packages: EngineeringEngineering (R0)