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Using Large Margin Nearest Neighbor Regression Algorithm to Predict Student Grades Based on Social Media Traces

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Book cover Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 617))

Abstract

Predicting students’ performance is a popular objective of learning analytics, aimed at identifying indicators for learning success. Various data mining approaches have been applied for this purpose on student data collected from learning management systems or intelligent tutoring systems. However, the emerging social media-based learning environments have been less explored so far. Hence, in this paper we present an approach for predicting students’ performance based on their contributions on wiki, blog and microblogging tool. An innovative algorithm (Large Margin Nearest Neighbor Regression) is applied, and comparisons with other algorithms are conducted. Very good correlation coefficients are obtained, outperforming commonly used regression algorithms. Overall, results indicate that students’ active participation on social media tools is a good predictor of learning performance.

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References

  1. Baker, R.S., Inventado, P.S.: Educational data mining and learning analytics. In: Larusson, J.A., White, B. (eds.) Learning Analytics: From Research to Practice, pp. 61–75. Springer, New York (2014)

    Google Scholar 

  2. Dyckhoff, A.L., Lukarov, V., Muslim, A., Chatti, M.A, Schroeder, U.: Supporting action research with learning analytics. In: Proceedings of the LAK 2013, pp. 220–229. ACM Press (2013)

    Google Scholar 

  3. Fancsali, S.: Variable construction for predictive and causal modeling of online education data. In: Proceedings of the LAK 2011, pp. 54–63. ACM Press (2011)

    Google Scholar 

  4. Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., Vuorikari, R.: Research evidence on the use of learning analytics - implications for education policy. In: Joint Research Centre Science for Policy Report (2016). doi:10.2791/955210

  5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  6. Leon, F., Curteanu, S.: Evolutionary algorithm for large margin nearest neighbour regression. In: Proceedings of the ICCCI 2015. LNAI, vol. 9329, pp. 286–296. Springer (2015)

    Google Scholar 

  7. Leon, F., Curteanu, S.: Large margin nearest neighbour regression using different optimization techniques. J. Intell. Fuzzy Syst. 32(2), 1321–1332 (2017)

    Article  Google Scholar 

  8. Popescu, E.: Providing collaborative learning support with social media in an integrated environment. World Wide Web 17(2), 199–212 (2014). Springer

    Article  Google Scholar 

  9. Roberge, D., Rojas, A., Baker, R.S.: Does the length of time off-task matter? In: Proceedings of the LAK 2012, pp. 234–237. ACM Press (2012)

    Google Scholar 

  10. Steiner, C., Kickmeier-Rust, M. Türker, M.A.: Review article about LA and EDM approaches (Deliverable D3.1) (2014). http://css-kmi.tugraz.at/mkrwww/leas-box/downloads/D3.1.pdf

  11. Wang, Y.H., Liao, H.C.: Data mining for adaptive learning in a TESL-based e-learning system. Expert Syst. Appl. 38(6), 6480–6485 (2011)

    Article  Google Scholar 

  12. Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)

    MATH  Google Scholar 

  13. Wolff, A., Zdrahal, Z., Nikolov, A., Pantucek, M.: Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In: Proceedings of the LAK 2013, pp. 145–149. ACM Press (2013)

    Google Scholar 

  14. Xing, W., Guo, R., Petakovic, E., Goggins, S.: Participation-based student final performance prediction model through interpretable genetic programming: Integrating learning analytics, educational data mining and theory. Comput. Hum. Behav. 47, 168–181 (2015)

    Article  Google Scholar 

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Acknowledgements

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-II-RU-TE-2014-4-2604.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Technical University “Gheorghe Asachi” of Iasi, Iasi, Romania

    Florin Leon

  2. Computers and Information Technology Department, University of Craiova, Craiova, Romania

    Elvira Popescu

Authors
  1. Florin Leon
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  2. Elvira Popescu
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Corresponding author

Correspondence to Elvira Popescu .

Editor information

Editors and Affiliations

  1. University of L'Aquila, Department of Life, Health and Environmental Sciences, L'Aquila, Italy

    Pierpaolo Vittorini

  2. Free University of Bozen-Bolzano, Computer Science Faculty, Bolzano, Italy

    Rosella Gennari

  3. University of L'Aquila, Department of Information Engineering, Computer Science and Mathematics, L'Aquila, Italy

    Tania Di Mascio

  4. University of Salamanca, Department of Computer Science and Automation Control, Salamanca, Spain

    Sara Rodríguez

  5. University of Salamanca, Department of Computer Science and Automation Control, Salamanca, Spain

    Fernando De la Prieta

  6. Politécnico do Porto, GECAD, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Carlos Ramos

  7. Federal University of Santa Catarina, Department of Computer Science and Statistics, Florianópolis,SC, Brazil

    Ricardo Azambuja Silveira

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Leon, F., Popescu, E. (2017). Using Large Margin Nearest Neighbor Regression Algorithm to Predict Student Grades Based on Social Media Traces. In: Vittorini, P., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning. MIS4TEL 2017. Advances in Intelligent Systems and Computing, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-319-60819-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-60819-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60818-1

  • Online ISBN: 978-3-319-60819-8

  • eBook Packages: EngineeringEngineering (R0)

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