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International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.9, No.8, Aug. 2017

Evaluation of Data Mining Techniques for Predicting Student’s Performance

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Author(s)

Mukesh Kumar, A.J. Singh

Index Terms

Educational Data Mining;Random Forest;Decision Tree;Naive Bayes;Bayes Network

Abstract

This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.

Cite This Paper

Mukesh Kumar, A.J. Singh,"Evaluation of Data Mining Techniques for Predicting Student’s Performance", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.8, pp.25-31, 2017.DOI: 10.5815/ijmecs.2017.08.04

Reference

[1]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

[2]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.

[3]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

[4]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

[5]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

[6]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.

[7]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

[8]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.

[9]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).

[10]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

[11]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

[12]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

[13]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 

[14]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

[15]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/).

[16]Maria Goga, Shade Kuyoro, Nicolae Goga, A recommended for improving the student academic performance, Social and Behavioral Sciences 180 (2015) 1481 – 1488 

[17]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.

[18]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

[19]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

[20]Asif, R., Merceron, A., & Pathan, M. (2014). Investigating performances' progress of students. In Workshop Learning Analytics, 12th e_Learning Conference of the German Computer Society (DeLFI 2014) (pp. 116e123). Freiburg, Germany, September 15.

[21]Asif, R., Merceron, A., & Pathan, M. (2015a). Investigating performance of students: A longitudinal study. In 5th international conference on learning analytics and knowledge (pp. 108e112). Poughkeepsie, NY, USA, March 16-20 http://dx.doi.org/10.1145/2723576.2723579.

[22]Asif, R., Merceron, Syed Abbas Ali, Najmi Ghani Haider. Analyzing undergraduate students' performance using educational data mining. Computer & Education 113(2017) 177-194, http://dx.doi.org/10.1016/j.compedu.2017.05.007

[23]Mukesh Kumar, Prof. A.J. Singh, Dr. Disha Handa. Literature Survey on Educational Dropout Prediction. I.J. Education and Management Engineering, 2017, 2, 8-19 Published Online March 2017 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2017.02.02

[24]Mukesh Kumar, Prof. A.J. Singh, Dr. Disha Handa. Literature Survey on Student's performance prediction in Education using Data Mining Techniques. I.J. Education and Management Engineering. (Accepted) in MECS (http://www.mecs-press.net).