Advertisement

Analysis of Student Achievement Scores: A Machine Learning Approach

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

Abstract

Educational Data Mining (EDM) is an emerging discipline of increasing interest due to several factors, such as the adoption of learning management systems in education environment. In this work we analyze the predictive power of continuous evaluation activities with respect the overall student performance in physics course at Universidad Loyola Andalucíıa, in Seville, Spain. Such data was collected during the fall semester of 2018 and we applied several classification algorithms, as well as feature selection strategies. Results suggest that several activities are not really relevant and, so, machine learning techniques may be helpful to design new relevant and non-redundant activities for enhancing student knowledge acquisition in physics course. These results may be extrapolated to other courses.

Keywords

Educational Data Mining Classification Feature selection 

References

  1. 1.
    Algarni, A.: Data mining in education. Int. J. Adv. Comput. Sci. Appl. 7(6), 456–461 (2016)Google Scholar
  2. 2.
    Alonso, D.B., Lopez-Cobo, I., Gomez-Rey, P., Fernandez-Navarro, F., Barbera, E.: A new tool to create personalized student material for automated grading, Working paper 2019Google Scholar
  3. 3.
    Asif, R., Merceron, A., Ali, S.A., Haider, N.G.: Analyzing undergraduate students’ performance using educational data mining. Comput. Educ. 113, 177–194 (2017)CrossRefGoogle Scholar
  4. 4.
    Baker, R.S., Yacef, K.: The state of educational data mining in 2009: a review and future visions. JEDM| J. Educ. Data Min. 1(1), 3–17 (2009)Google Scholar
  5. 5.
    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)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hall, M.A.: Correlation-based feature subset selection for machine learning. Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1998)Google Scholar
  7. 7.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)zbMATHGoogle Scholar
  8. 8.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)zbMATHGoogle Scholar
  9. 9.
    Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst. Appl. 41(4), 1432–1462 (2014)CrossRefGoogle Scholar
  10. 10.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  11. 11.
    Ramaswami, M., Bhaskaran, R.: A study on feature selection techniques in educational data mining. J. Comput. 1(1), 7–11 (2009)Google Scholar
  12. 12.
    Saa, A.A.: Educational data mining & students performance prediction. Int. J. Adv. Comput. Sci. Appl. 7(5), 212–220 (2016)Google Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  15. 15.
    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
  16. 16.
    Ward, D.: einstruction: Classroom performance system (computer software). EInstruction Corporation, Denton, TX (2007)Google Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)MathSciNetzbMATHGoogle Scholar
  19. 19.
    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

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Division of Computer ScienceUniversidad Pablo de OlavideSevilleSpain
  2. 2.Universidad Loyola AndalucíaSevilleSpain

Personalised recommendations