Skip to main content
Book cover

Computational Intelligence in Security for Information Systems Conference

International Conference on EUropean Transnational Education

CISIS 2019, ICEUTE 2019: International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019) pp 285–294Cite as

Analysis of Teacher Training in Mathematics in Paraguay’s Elementary Education System Using Machine Learning Techniques

  • 504 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 951)

Abstract

In Paraguay, despite the fact that Elementary Education is one of the cornerstones of the educational system, it has not always received the recognition it deserves. Recently, the Paraguayan government has started to focus its effort on evaluating the quality of its education system through the analysis of some factors of the teachers. In this work, which falls into the context of such project, we study the ability to understand the different evaluation types structures in mathematics. The data, collected from elementary mathematics teachers from all over the country, is analyzed by applying an education data mining (EDM) approach. Results show that not all questions are equally important and it is necessary to continue through different lines of action to get insight about the action policy to improve the educational system quality.

Keywords

  • Educational data mining
  • Feature selection
  • Classification

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-20005-3_29
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-20005-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Asif, R., Merceron, A., Ali, S.A., Haider, N.G.: Analyzing undergraduate students’ performance using educational data mining. Comput. Educ. 113, 177–194 (2017)

    CrossRef  Google Scholar 

  2. Castro, F., Vellido, A., Nebot, À., Mugica, F.: Applying data mining techniques to e-learning problems. In: Evolution of Teaching and Learning Paradigms in Intelligent Environment, pp. 183–221. Springer (2007)

    Google Scholar 

  3. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Willey, New Yotk (1973)

    MATH  Google Scholar 

  4. García-López, F.C., García-Torres, M., Melián-Batista, B., Pérez, J.A.M., Moreno-Vega, J.M.: Solving the feature selection problem by a parallel scatter search. Eur. J. Oper. Res. 169(2), 477–489 (2006)

    CrossRef  MathSciNet  Google Scholar 

  5. Hall, M.A.: Correlation-based feature subset selection for machine learning. Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1998)

    Google Scholar 

  6. Hegazi, M.O., Abugroon, M.A.: The state of the art on educational data mining in higher education. Int. J. Comput. Trends Technol. 31(1), 46–56 (2016)

    CrossRef  Google Scholar 

  7. Kabra, R., Bichkar, R.: Performance prediction of engineering students using decision trees. Int. J. Comput. Appl. 36(11), 8–12 (2011)

    Google Scholar 

  8. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)

    MATH  Google Scholar 

  9. Saeys, Y., Abeel, T., van de Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, vol. 5212. Lecture Notes In Artificial Intelligence, pp. 313–325 (2008)

    Google Scholar 

  10. Shahiri, A.M., Husain, W., et al.: A review on predicting student’s performance using data mining techniques. Procedia Comput. Sci. 72, 414–422 (2015)

    CrossRef  Google Scholar 

  11. UNESCO: TERCE: associated factors, executive summary (2015)

    Google Scholar 

  12. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience, New York (1998)

    MATH  Google Scholar 

  13. Velmurugan, T., Anuradha, C.: Performance evaluation of feature selection algorithms in educational data mining. Int. J. Data Min. Tech. Appl. 5(02), 131–139 (2016)

    Google Scholar 

  14. Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)

    MathSciNet  MATH  Google Scholar 

  15. Zaffar, M., Hashmani, M.A., Savita, K.: Performance analysis of feature selection algorithm for educational data mining. In: 2017 IEEE Conference on Big Data and Analytics (ICBDA), pp. 7–12. IEEE (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Americana, Asunción, Paraguay

    Viviana Elizabeth Jiménez Chaves, José Luis Vázquez Noguera & Andrea Paola Riego Esteche

  2. Division of Computer Science, Universidad Pablo de Olavide, 41013, Seville, Spain

    Miguel García-Torres & Federico Divina

  3. Dpto. de Matemáticas, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Asunción, San Lorenzo, Paraguay

    César D. Cabrera Oviedo

  4. Dpto. de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, 41018, Seville, Spain

    Manuel Marrufo-Vázquez

Authors
  1. Viviana Elizabeth Jiménez Chaves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel García-Torres
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Luis Vázquez Noguera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. César D. Cabrera Oviedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrea Paola Riego Esteche
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Federico Divina
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Manuel Marrufo-Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miguel García-Torres .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Prof. Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Chaves, V.E.J. et al. (2020). Analysis of Teacher Training in Mathematics in Paraguay’s Elementary Education System Using Machine Learning Techniques. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_29

Download citation