Predicting Student Performance from Combined Data Sources

  • Annika Wolff
  • Zdenek Zdrahal
  • Drahomira Herrmannova
  • Petr Knoth
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 524)

Abstract

This chapter will explore the use of predictive modeling methods for identifying students who will benefit most from tutor interventions. This is a growing area of research and is especially useful in distance learning where tutors and students do not meet face to face. The methods discussed will include decision-tree classification, support vector machine (SVM), general unary hypotheses automaton (GUHA), Bayesian networks, and linear and logistic regression. These methods have been trialed through building and testing predictive models using data from several Open University (OU) modules. The Open University offers a good test-bed for this work, as it is one of the largest distance learning institutions in Europe. The chapter will discuss how the predictive capacity of the different sources of data changes as the course progresses. It will also highlight the importance of understanding how a student’s pattern of behavior changes during the course.

Keywords

Predictive modeling Education Virtual learning environment Student outcome 

Abbreviations

ANOVA

Analysis of variance

CMS

Course management system

CS

Course signals

GUHA

General unary hypotheses automaton

MOOC

Massive open online course

OU

Open university

SVM

Support vector machine

TMA

Tutor marked assessment

VLE

Virtual learning environment

References

  1. 1.
    Kabra, R.R., Bichkar, R.S.: Performance prediction of engineering students using decision trees. Int. J. Comput. Appl. 36(11), 8–12 (2011)Google Scholar
  2. 2.
    Baradwaj, B., Pal, S.: Mining educational data to analyze student’s performance. Int. J. Adv. Comput. Sci. Appl. 2(6), 63–69 (2011)Google Scholar
  3. 3.
    Pandey, M., Sharma, V.K.: A decision tree algorithm pertaining to the student performance analysis and prediction. Int. J. Comput. Appl. 61(13), 1–5 (2013)Google Scholar
  4. 4.
    Baepler, P., Murdoch, C.J.: Academic analytics and data mining in higher education. Int. J. Sch. Teach. Learn. 4(2), 1–9 (2010)Google Scholar
  5. 5.
    Arnold, K.E., Pistilli, M.D.: Course signals at purdue: using learning analytics to increase student success. In: 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM, New York (2012)Google Scholar
  6. 6.
    Pistilli, M.D., Arnold, K.E.: Purdue signals: mining real-time academic data to enhance student success. About Campus 15(3), 22–24 (2010)CrossRefGoogle Scholar
  7. 7.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  8. 8.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  9. 9.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  10. 10.
    Hájek, P., Holeňa, M., Rauch, J.: The GUHA method and its meaning for data mining. J. Comput. Syst. Sci. 76(1), 34–48 (2010)Google Scholar
  11. 11.
    Rauch, J.: GUHA method and the LISp-miner system. In: Observational Calculi and Association Rules. Studies of Computational Intelligence, vol. 469, pp. 233–260. Springer, Heidelberg (2013)Google Scholar
  12. 12.
    Koller, D., Friedman, F.: Probabilistic Graphical Models. MIT Press, Cambridge (2009)MATHGoogle Scholar
  13. 13.
    Bishop, C. M.: A new framework for machine learning. In: Zurada, J.M., Yen, G.G., Wang, J. (eds.) Computational Intelligence: Research Frontiers, IEEE World Congress on Computational Intelligence. LNCS, vol. 5050, pp. 1–24. Springer, Heidelberg (2008)Google Scholar
  14. 14.
    Minka, T., Winn, J., Guiver, J., Knowles, D.: Infer.NET 2.5, Microsoft Research, Cambridge (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Annika Wolff
    • 1
  • Zdenek Zdrahal
    • 1
  • Drahomira Herrmannova
    • 1
  • Petr Knoth
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

Personalised recommendations