Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning

  • Celia González Nespereira
  • Esraa Elhariri
  • Nashwa El-Bendary
  • Ana Fernández Vilas
  • Rebeca P. Díaz Redondo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 407)

Abstract

Nowadays, recognizing and predicting students learning achievement introduces a significant challenge, especially in blended learning environments, where online (web-based electronic interaction) and offline (direct face-to-face interaction in classrooms) learning are combined. This paper presents a Machine Learning (ML) based classification approach for students learning achievement behavior in Higher Education. In the proposed approach, Random Forests (RF) and Support Vector Machines (SVM) classification algorithms are being applied for developing prediction models in order to discover the underlying relationship between students past course interactions with Learning Management Systems (LMS) and their tendency to pass/fail. In this paper, we considered daily students interaction events, based on time series, with a number of Moodle LMS modules as the leading characteristics to observe students learning performance. The dataset used for experiments is constructed based on anonymized real data samples traced from web-log files of students access behavior concerning different modules in a Moodle online LMS throughout two academic years. Experimental results showed that the proposed RF classification system has outperformed the typical SVMs classification algorithm.

Keywords

Learning analytics Blended learning Features extraction Learning management systems (LMS) Moodle Random forests (RF) Support vector machines (SVMS) 

Notes

Acknowledgments

This work is funded by: the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC); the Spanish Government and the European Regional Development Fund (ERDF) under project TACTICA; and the Spanish Ministry of Economy and Competitiveness under the National Science Program (TEC2014-54335-C4-3-R). This work is also partially funded by the European Commission under the Erasmus Mundus GreenIT project (3772227-1-2012-ES-ERA MUNDUS-EMA21). The authors also thank GRADIANT for its computing support and the University of Vigo for its e-learning service for their support.

References

  1. 1.
    Kim, J.H., Park, Y., Song, J., Jo, I.-H., Predicting students’ learning performance by using online behavior patterns in blended learning environments: comparison of two cases on linear and non-linear model. In: The International Conference on Educational Data Mining (EDM 2014), London, United Kingdom (2014)Google Scholar
  2. 2.
    Liebowitz, J., Frank, M.: Knowledge Management and E-learning. CRC Press (2010)Google Scholar
  3. 3.
    Romero, C., Ventura, S., Garca, E.: Data mining in course management systems: moodle case study and tutorial. Comput. Educ. 51, 368–384 (2008)CrossRefGoogle Scholar
  4. 4.
    Romero, C., Ventura, S.: Data mining in education. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3, 12–27 (2013)CrossRefGoogle Scholar
  5. 5.
    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), 601–618 (2010)CrossRefGoogle Scholar
  6. 6.
    van Barneveld, A., Arnold, K.E., Campbell, J.P.: Analytics in higher education: establishing a common language. Educause Learn. Initiat. 1, 1–11 (2012)Google Scholar
  7. 7.
    Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief. Office of Educational Technology, U.S. Department of Education (2012)Google Scholar
  8. 8.
    Wu, Q., Zhou, D.-X.: Analysis of support vector machine classification. J. Comput. Anal. Appl. 8, 99–119 (2006)MathSciNetMATHGoogle Scholar
  9. 9.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Abraham, A.: SVM-based soccer video summarization system. In: Proceedings of the Third IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2011), Salamanca, Spain, pp. 7–11 (2011)Google Scholar
  10. 10.
    Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Kim, T.H.: Machine learning-based soccer video summarization system. In: Proceedings of the Multimedia, Computer Graphics and Broadcasting FGIT-MulGraB (2), Jeju Island, Korea, vol. 263, pp. 19–28. Springer (2011)Google Scholar
  11. 11.
    Suralkar, S.R., Karode, A.H., Pawade, P.W.: Texture image classification using support vector machine. Int. J. Comput. Appl. Technol. 3(1), 71–75 (2012)Google Scholar
  12. 12.
    Kulkarni, V.Y., Sinha, P.K.: Efficient learning of random forest classifier using disjoint partitioning approach. In: Proceedings of the World Congress on Engineering, vol. 2 (2013)Google Scholar
  13. 13.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: IEEE 11th International Conference on Computer Vision (2007)Google Scholar
  14. 14.
    Nisbet, R., Elder, J., Miner, G.: Handbook of Statistical Analysis and Data Mining Applications. Academic Press (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Celia González Nespereira
    • 1
  • Esraa Elhariri
    • 2
    • 4
  • Nashwa El-Bendary
    • 1
    • 3
    • 4
  • Ana Fernández Vilas
    • 1
  • Rebeca P. Díaz Redondo
    • 1
  1. 1.I&C Laboratory AtlantTIC Research CenterUniversity of VigoVigoSpain
  2. 2.Faculty of Computers and InformationFayoum UniversityFayoumEgypt
  3. 3.Arab Academy for Science,Technology, and Maritime TransportCairoEgypt
  4. 4.Scientific Research Group in Egypt (SRGE)CairoEgypt

Personalised recommendations