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Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

by Fatiha Tayeb Bousbahi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 6
Year of Publication: 2017
Authors: Fatiha Tayeb Bousbahi
10.5120/ijca2017916044

Fatiha Tayeb Bousbahi . Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases. International Journal of Computer Applications. 180, 6 ( Dec 2017), 14-21. DOI=10.5120/ijca2017916044

@article{ 10.5120/ijca2017916044,
author = { Fatiha Tayeb Bousbahi },
title = { Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 180 },
number = { 6 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 14-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number6/28804-2017916044/ },
doi = { 10.5120/ijca2017916044 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:59:54.578401+05:30
%A Fatiha Tayeb Bousbahi
%T Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 6
%P 14-21
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Universities offer a wide selection of courses to students studying in different fields; however, this has generated various unintended problem such as students struggling, dropping or failing. Universities also, own large amounts of historical data about students containing rich knowledge that can be used to facilitate a broad range of educational research and analytics study in order to enhance student performance. Knowledge discovery in databases (KDD) is the process of finding comprehensible patterns that can be interpreted as useful or interesting knowledge. One component in this process is Data Mining which is the application of machine learning algorithms for extracting patterns from data. In this context, data mining techniques are used to answer educational research questions that highlight students’ success in higher education. Several challenges are facing higher education, one of which is finding the most effective sequence of modules for students for each semester. This finding may help students to improve their performance. In this research we investigate dependency between set of courses chosen by students at each level and their performance using a classification technique in order to build a performance prediction model, which is based on previous students’ academic records. The finding of the study furnishes useful information to advisors in educational institution and valuable knowledge to feed analytics based systems and learning recommender systems to assist students selecting future modules to obtain optimum pathways courses.

References
  1. Al-Saleem M., Al-Kathiry N., Al-Osimi S. and Badr G. “Mining Educational Data to Predict Students' Academic Performance”. Springer, 2015.
  2. Bousbia, N., Belamri, I: Which Contribution Does EDM Provide to Computer-Based Learning Environments?. Educational Data Mining: Applications and Trends. Springer, 2014.
  3. B a k e r, R., Y a c e f, K. “The State of Educational Data Mining in 2009: A Review and Future Visions”. Journal of Educational Data Mining, Vol. 1, Issue 1, pp.3-17, October 2009.
  4. Bayer J. Bydzovska, H., Geryk, J., Obsivac, T., Popelinsky, L. "Predicting dropout from social behavior of students". Proceedings of the 5th International Conference on Educational Data Mining, Crete, Greece, pp.103–109, 2012.
  5. Bin Mat, U., Buniyamin N., Arsad P. M., Kassim R., 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.
  6. Chanamarn, N., Tamee, K. "Enhancing Efficient Study Plan for Student with Machine Learning Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.3, pp.1-9, 2017.DOI: 10.5815/ijmecs.2017.03.01
  7. Fayyad, U. Piatetsky-Shapiro, G. and Smyth, P. “From Data Mining to Knowledge Discovery in Databases”. AI Magazine Volume 17 Number 3, 1996.
  8. Guruler, H. and Istanbullu, A. “Modeling Student Performance in Higher Education Using Data Mining”. A. Peña-Ayala (ed.), Educational Data Mining, Studies in Computational Intelligence 524, DOI: 10.1007/978-3-319-02738-8_4, Springer International Publishing Switzerland 2014
  9. Grumbach, S. Valduriez, P. "Les données en question". [Online]. Available: https://interstices.info/jcms/p_84069/les-donnees-en-question [Accessed on 2 Oct. 2017].
  10. Hall, M., Eibe F., Holmes, G, Reutemann, Bernhard P. and Witten, Ian H. Data mining with WEKA, update.
  11. Hssina, B., Merbouha, A., Ezzikouri, H. and Erritali, M. A comparative study of decision tree ID3 and C4.5. International Journal of Advanced Computer Science and Applications, 4(2), 13-19, 2014.
  12. Herlocker J.L., Konstan J.A., Terveen L.G., Riedl J.T. “Evaluating Collaborative Filtering Recommender Systems”, ACM Transactions on Information Systems, Vol. 22, No. 1, January 2004, Pages 5–53.
  13. Ian.H. Wi t t e n, E. F r a n k, Mark A. Hall. “Data Mining: Practical Machine Learning Tools and Techniques”. 3rd edition, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA ©2011.
  14. Ian.H. Wi t t e n, E. F r a n k, Mark A. Hall. “Data Mining: Practical Machine Learning Tools and Techniques”. 3rd edition, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA ©2011.
  15. Josip Mesarić, and Dario Šebal, Decision trees for predicting the academic success of students, 2016.
  16. Kabakchieva D. “Predicting Student Performance by Using Data Mining Methods for Classification”. Cybernetics and Information Technologies • Volume 13, No 1, 2013.
  17. Larson, B., English, D., Purington, P. “Delivering Business Intelligence with Microsoft SQL Server 2012”. McGraw-Hill, New York, 2012.
  18. Márquez-vera, C., Cano, A., Romero, C , Noaman, A., Fardoun, Habib M.and Ventura, S. “Early dropout prediction using data mining: a case study with high school students”. Expert Systems, Vol. 33, No. 1, February 2016.
  19. MATLAB, “MATLAB Environment”, from http://www.mathworks.com/products/matlab/, 2016.
  20. Minaei-bidgoli, B., Kashy, D. A., Kortemeye,r G., Punch, W.F. “Predicting Student Performance: An Application of Data Mining Methods with the Educational Web-Based System LON-CAPA”. 33rd ASEE/ IEEE Frontiers in Education Conference, Nov. 5-8, 2003, boulder, co.
  21. Mueen, A. , Zafar, B., Manzoor, U. “Modeling and Predicting Students' Academic Performance Using Data Mining Techniques”. I.J. Modern Education and Computer Science, 2016, 11, pp. 36-42 Published Online November 2016 in MECS, DOI: 10.5815/ijmecs.2016.11.05
  22. Pena-Ayala, A. “Educational data mining: applications and trends”. 2013
  23. Quinlan, R. J. (1996). Improved Use of Continuous Attributes in C4.5. Journal of Arti, 4, 77-90.
  24. R o m e r o, C., S. V e n t u r a. Educational Data Mining: A Survey from 1995 to 2005. – Expert Systems with Applications, Vol. 33, pp.135-146, 2007.
  25. Romero, C., Ventura, S. Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), pp. 601–618, 2010.
  26. Romero, C., & Ventura, S. “Data mining in Education,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), pp.12–27, 2013.
  27. Russel S., Norvig P. “Artificial Intelligence: A novel Approach”. Third edition, 2010.
  28. Shahiri A., Husaina W., Abdul Rashida,N.. “A Review on Predicting Student’s Performance using Data Mining Techniques”. Procedia Computer Science 72, pp. 414 – 422, 2015.
  29. SELF-STUDY REPORT Bachelor of Science Program in Information Technology July 2012.
  30. Vialardi C., Bravo J., Shafti, L., Ortigosa, A. “Recommendation in Higher Education Using Data Mining Techniques”. International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009).
  31. Weka, 2017. Retrieved from http://www.cs.waikato.ac.nz/ml/weka/.
  32. Xie N., Liu Y. “Review of decision trees”, in: Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 5, 2010, pp. 105–109.
  33. Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S.Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, and Dan Steinberg, 2008. "Top 10 algorithms in data mining", Knowledge and Information Systems, 14, 1: 1–37.
Index Terms

Computer Science
Information Sciences

Keywords

Student Performance Sequence of Modules Knowledge Discovery in Databases Educational Data Mining Classification Prediction.

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