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International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.7, No.6, Nov. 2017

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

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Author(s)

Mukesh Kumar, A.J. Singh, Disha Handa

Index Terms

Educational Data Mining;Prediction Techniques;Student attributes;Classification

Abstract

One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student’s performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution. 

Cite This Paper

Mukesh Kumar, A.J. Singh, Disha Handa,"Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques", International Journal of Education and Management Engineering(IJEME), Vol.7, No.6, pp.40-49, 2017.DOI: 10.5815/ijeme.2017.06.05

Reference

[1]Mihai Dascalu and Elvira Popescu et. al., Predicting Academic Performance Based on Students’ Blog and Microblog Posts, Springer International Publishing Switzerland 2016 K. Verbert et al. (Eds.): EC-TEL 2016, LNCS 9891, pp. 370–376, 2016. DOI: 10.1007/978-3-319-45153-4_29.

[2]U. bin Mat, N. Buniyamin, P. M. Arsad, R. Kassim, An overview of using academic analytics to predict and improve students’ achievement: A proposed proactive intelligent intervention, in: Engineering Education (ICEED), 2013 IEEE 5th Conference on, IEEE, 2013, pp. 126–130.

[3]Randa Kh. Hemaid and Alaa M. El-Halees, Improving Teacher Performance using Data Mining, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 2, February 2015.

[4]Farhana Sarker, Thanassis Tiropanis and Hugh C Davis, Students’ Performance Prediction by Using Institutional Internal and External Open Data Sources, http://eprints.soton.ac.uk/353532/1/Students' mark prediction model.pdf, 2013.

[5]D. M. D. Angeline, Association rule generation for student performance analysis using an apriori algorithm, The SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 1 (1) (2013) p12–16.

[6]Abeer Badr El Din Ahmed and Ibrahim Sayed Elaraby, Data Mining: A prediction for Student's Performance Using Classification Method, World Journal of Computer Application and Technology 2(2): 43-47, 2014.

[7]Fadhilah Ahmad, Nur Hafieza Ismail and Azwa Abdul Aziz, The Prediction of Students’ Academic Performance Using Classification Data Mining Techniques, Applied Mathematical Sciences, Vol. 9, 2015, no. 129, 6415 - 6426HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2015.53289.

[8]Mashael A. Al-Barrak and Mona S. Al-Razgan, predicting students’ performance through classification: a case study, Journal of Theoretical and Applied Information Technology 20th May 2015. Vol.75. No.2.

[9]Edin Osmanbegović and Mirza Suljic, DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE, Economic Review – Journal of Economics and Business, Vol. X, Issue 1, May 2012.

[10]Raheela Asif, Agathe Merceron, Mahmood K. Pathan, Predicting Student Academic Performance at Degree Level: A Case Study, I.J. Intelligent Systems and Applications, 2015, 01, 49-61 Published Online December 2014 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2015.01.05.

[11]Mohammed M. Abu Tair, Alaa M. El-Halees, Mining Educational Data to Improve Students’ Performance: A Case Study, International Journal of Information and Communication Technology Research, ISSN 2223-4985, Volume 2 No. 2, February 2012.

[12]Azwa Abdul Aziz, Nor Hafieza Ismailand Fadhilah Ahmad, First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms, Proceeding of the International Conference on Artificial Intelligence and Computer Science(AICS 2014), 15 - 16 September 2014, Bandung, INDONESIA. (e-ISBN978-967-11768-8-7).

[13]Kolo David Kolo, Solomon A. Adepoju, John Kolo Alhassan, A Decision Tree Approach for Predicting Students Academic Performance, I.J. Education and Management Engineering, 2015, 5, 12-19 Published Online October 2015 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2015.05.02.

[14]Dr Pranav Patil, a study of student’s academic performance using data mining techniques, international journal of research in computer applications and robotics, ISSN 2320-7345, vol.3 issue 9, pg.: 59-63 September 2015.

[15]Jyoti Bansode, Mining Educational Data to Predict Student‘s Academic Performance, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, Volume: 4 Issue: 1, 2016.

[16]R. Sumitha and E.S. Vinoth kumar, Prediction of Students Outcome Using Data Mining Techniques, International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2, Issue-6,June 2016 ISSN: 2395-3470.

[17]Karishma B. Bhegade and Swati V. Shinde, Student Performance Prediction System with Educational Data Mining, International Journal of Computer Applications (0975 – 8887) Volume 146 – No.5, July 2016.

[18]Mrinal Pandey and S. Taruna, Towards the integration of multiple classifiers pertaining to the Student's performance prediction, http://dx.doi.org/10.1016/j.pisc.2016.04.076 2213-0209/© 2016 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

[19]Maria Goga, Shade Kuyoro, Nicolae Goga, A recommender for improving the student academic performance, Social and Behavioural Sciences 180 (2015) 1481 – 1488.

[20]Anca Udristoiu, Stefan Udristoiu, and Elvira Popescu, Predicting Students’ Results Using Rough Sets Theory, E. Corchado et al. (Eds.): IDEAL 2014, LNCS 8669, pp. 336–343, 2014. © Springer International Publishing Switzerland 2014.

[21]Parneet Kaur, Manpreet Singh, Gurpreet Singh Josan, Classification and prediction based data mining algorithms to predict slow learners in education sector, Procedia Computer Science 57 (2015) 500 – 508.

[22]M. Durairaj and C. Vijitha, Educational Data mining for Prediction of Student Performance Using Clustering Algorithms, International Journal of Computer Science and Information Technologies, Vol. 5 (4), 2014, 5987-5991.

[23]Mohammed I. Al-Twijri and Amin Y. Noaman, A New Data Mining Model Adopted for Higher Institutions, Procedia Computer Science 65 ( 2015 ) 836 – 844, doi: 10.1016/j.procs.2015.09.037.

[24]Maria Koutina and Katia Lida Kermanidis, Predicting Postgraduate Students’ Performance Using Machine Learning Techniques, L. Iliadis et al. (Eds.): EANN/AIAI 2011, Part II, IFIP AICT 364, pp. 159–168, 2011. © IFIP International Federation for Information Processing 2011.