Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Systems Design and Applications

ISDA 2017: Intelligent Systems Design and Applications pp 650–660Cite as

Selecting Relevant Educational Attributes for Predicting Students’ Academic Performance

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

Predicting students’ academic performance is one of the oldest and most popular applications of educational data mining. It helps to estimate the unknown evaluation of a student’s performance. However, a huge amount of data with different formats and from multiple sources may contain a large number of features supposed as not-relevant that could influence the prediction results. The main objective of this paper is to improve the effectiveness of a predictive model for students’ academic performance. For this purpose, we propose a methodology to carry out a comparative study for evaluating the influence of feature selection techniques on the prediction of students’ academic performance. In our study, F-measure parameter is used to evaluate the effectiveness of the selected techniques. Two real data sources are used in this work, Mathematics and language courses. The outcomes are compared and discussed in order to identify the technique that has the best influence for an accurate predictive model.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.cs.waikato.ac.nz/ml/weka/arff.html.

  2. 2.

    European exchange programme that enables student exchange in 31 countries.

References

  1. Romero, C., Ventura, S.: Data mining in education. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 3(1), 12–27 (2013)

    Article  Google Scholar 

  2. Abid, A., Kallel, I., BenAyed, M.: Teamwork construction in e-learning system: a systematic literature review. In: 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, pp. 1–7 (2016)

    Google Scholar 

  3. Mitra, P., Murthy, C., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)

    Article  Google Scholar 

  4. Miller, A.: Subset Selection in Regression. CRC Press, Boca Raton (2002)

    Book  MATH  Google Scholar 

  5. Hall, M.A.: Correlation based feature selection for machine learning (1999)

    Google Scholar 

  6. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, pp. 249–256 (1992)

    Google Scholar 

  7. Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning, pp. 171–182. Springer (1994)

    Google Scholar 

  8. Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, Amsterdam (2014)

    Google Scholar 

  9. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, vol. 2. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  10. Ramaswami, M., Bhaskaran, R.: A study on feature selection techniques in educational data mining. arXiv preprint arXiv:0912.3924 (2009)

  11. Velmurugan, T., Anuradha, C.: Performance evaluation of feature selection algorithms in educational data mining. Perform. Eval. 5(02) (2016)

    Google Scholar 

  12. Costa, E.B., Fonseca, B., Santana, M.A., de Araújo, F.F., Rego, J.: Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Comput. Hum. Behav. 73, 247–256 (2017)

    Article  Google Scholar 

  13. Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013)

    Article  Google Scholar 

  14. Noura, A., Shili, H., Romdhane, L.B.: Reliable attribute selection based on random forest (RASER). In: International Conference on Intelligent Systems Design and Applications, pp. 11–24. Springer (2017)

    Google Scholar 

  15. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  16. Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123 (1995)

    Google Scholar 

  17. Friedman, J., Hastie, T., Tibshirani, R., et al.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28(2), 337–407 (2000)

    Article  MATH  Google Scholar 

  18. Quinlan, J.R.: C4.5: Programming for Machine Learning, vol. 38. Morgan Kauffmann, Burlington (1993)

    Google Scholar 

  19. Smith, T.C., Frank, E.: Introducing machine learning concepts with WEKA. In: Statistical Genomics: Methods and Protocols, pp. 353–378 (2016)

    Google Scholar 

  20. Márquez-Vera, C., Morales, C.R., Soto, S.V.: Predicting school failure and dropout by using data mining techniques. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 8(1), 7–14 (2013)

    Article  Google Scholar 

  21. Gu, Q., Cai, Z., Zhu, L., Huang, B.: Data mining on imbalanced data sets. In: IEEE 2008 International Conference on Advanced Computer Theory and Engineering, ICACTE 2008, pp. 1020–1024 (2008)

    Google Scholar 

  22. Cortez, P., Silva, A.M.G.: Using data mining to predict secondary school student performance (2008)

    Google Scholar 

  23. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)

    MATH  Google Scholar 

  24. Volungevičienė, A., Daukšienė, E., Caldirola, E., Blanco, I.J.: Success factors for virtual mobility exchange on open educational resources (2014)

    Google Scholar 

  25. Chatty, A., Kallel, I., Alimi, A.M.: Counter-ant algorithm for evolving multirobot collaboration. In: Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology. ACM, pp. 84–89 (2008)

    Google Scholar 

  26. Abdelkefi, M., Kallel, I.: Towards a fuzzy multiagent tutoring system for M-learners’ emotion regulation. In: 2017 16th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, pp. 1–6 (2017)

    Google Scholar 

Download references

Acknowledgment

The authors express thanks to the Erasmus+ project for funding the research reported under the Grant Agreement number 2015-1-ES01-K107-015469.

Author information

Authors and Affiliations

  1. REGIM-Lab: Research Groups in Intelligent Machines, ENIS, University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Abir Abid, Ilhem Kallel & Mounir Benayed

  2. ISIMS: Higher Institute of Computer Science and Multimedia of Sfax, Route de Tunis Km 10 BP 242, 3021, Sfax, Tunisia

    Ilhem Kallel

  3. University of Granada, 18071, Granada, Spain

    Ignacio J. Blanco

  4. Computer Science and Communications Department, Faculty of Sciences of Sfax, University of Sfax, Route Sokra Km 3.5 BP 1171, 3000, Sfax, Tunisia

    Mounir Benayed

Authors
  1. Abir Abid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilhem Kallel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ignacio J. Blanco
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mounir Benayed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abir Abid .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abid, A., Kallel, I., Blanco, I.J., Benayed, M. (2018). Selecting Relevant Educational Attributes for Predicting Students’ Academic Performance. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-76348-4_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation