The week's pick
Reseach Article
Predicting Student's Performance in Education using Data Mining Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 19 |
| Year of Publication: 2019 |
| Authors: Sara Fatima, Salma Mahgoub |
10.5120/ijca2019919607
|
Sara Fatima, Salma Mahgoub . Predicting Student's Performance in Education using Data Mining Techniques. International Journal of Computer Applications. 177, 19 ( Nov 2019), 14-20. DOI=10.5120/ijca2019919607
Abstract
In this data world, where users spawn their digital footprint and generate a huge amount of unstructured data continuously with each activity, data mining techniques help in discovering interesting patterns, establishing relationships and unravel the problems through analysis, in different aspects of life. Educational data mining is a multidisciplinary research area, in which data from various educational organizations, is explored and made operational, for various facets concerned with the students, like predicting academic performance, analyse the learning pattern, solving e-learning issues, predict employability, visualize the critical courses affecting performance, investigate the reasons for student’s failure or drop out and thus make data-driven decisions to improve the institutions standards. This paper provides a brief overview of Data Mining tools and techniques, and its encroachment in the educational domain. It also proposes a simple framework using different variables which helps in predicting student’s academic success using two different algorithms: Decision Trees and Bayesian Network. Finally, a comparative analysis of accuracy is done. The results show that Bayesian Network outperforms the Decision Tress and gives better accuracy.
References
- Abu Saa. A, (2016), “Educational Data Mining & Students’ Performance Prediction”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5.
- Altujjar. Y,Altamimi. W, Turaiki.I & Razgan. M, (2016), “Predicting Critical Courses Affecting Students Performance: A Case Study”, ScienceDirect, Procedia Computer Science 82, pg: 65 – 71.
- Asif. R, Merceron.A, Ali.S and Haider.N, (2017), “Analyzing undergraduate students' performance using educational data mining”, Elsevier, Computers & Education 113 (2017) 177-194.
- Baker, R.S.& Yacef, K (2009), “The state of educational data mining in 2009: A review and future visions", JEDM-Journal of Educational Data Mining, Article 1, Vol 1, No 1.
- Berry M, Linoff G, (2004), “Data Mining Techniques: for marketing, sales, and customer relationship management”, Wiley, Publishing, chapter 1, page 10.
- Baker.R & Inventado. P, (2014), “Learning Analytics”, Chapter 4:“Educational Data Mining and Learning Analytics”, pp 61-75.
- Daud. A, Aljohani. N, Abbasi.R, Lytras. M, Abbas. F & Alowibdi.J, (2017), “Predicting Student Performance using Advanced Learning Analytics”, International World Wide Web Conference Committee (IW3C2), ACM 978-1-4503-4914-7/17/04.
- Gupta. G.K (2014), 3rd edition, “Introduction to Data mining with Case Studies, PHI Learning Private Limited, chapter 1, pag:3.
- Han. J, & Kamber. M, (2000), “Data mining: Concepts and Techniques”, Morgan Kaufmann Publishing, chapter 1: Introduction page:6.
- Kalbande.N, Handa.C, (2015), “Developing a model to predict Employability of Engineering Students in Campus Placement for IT Sector”, International Journal of Advance Research in Engineering, Science & Technology(IJAREST), ISSN(O):2393-9877, ISSN(P): 2394-2444
- Kolo. D, Adepoju. S & Alhassan. J (2015), “A Decision Tree Approach for Predicting Students Academic Performance”, I.J. Education and Management Engineering, Published Online October 2015 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2015.05.02.
- Khasanah. A & Harwati (2017), “A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques”, IOP Conference Series: Materials Science and Engineering 215, doi:10.1088/1757-899X/215/1/012036.
- Nithya. P, Umamaheswari. B & Umadevi.A (2016), “A Survey on Educational Data Mining in Field of Education Department of CS”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 5, Issue 1.
- Sorour.S, Goda.K &Mine.T, (2017), “Comment Data Mining to Estimate Student Performance Considering Consecutive Lessons”, Journal of Educational Technology & Society, Vol. 20, No. 1, pp. 73-86.
- Shahiria. A,Husaina. W, Rashida.N,(2015), “A Review on Predicting Student’s Performance using Data Mining Techniques”, The Third Information Systems International Conference, Elsevier, (http://creativecommons.org/licenses/by-nc-nd/4.0/)
- Sembiring.S, Zarlis.M, Hartama.D,, Ramliana S & Wani.E, (2011), “Prediction of Student Academic Performance by an application of Data Mining”, International Conference on Management and Artificial Intelligence, IPEDR vol.6.
- Romero.C, Ventura. S, (2007), “Educational Data Mining: A Survey from 1995 to 2005”, Expert System with Applications, Vol.33, pp 135-146.
- Oyerinde O. D, Chia P.A, (2017), “Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression”, International Journal of Computer Applications (0975 – 8887) Volume 157 – No 4.
Index Terms
Keywords