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Educational Data Mining pp 175–202Cite as

Predicting Student Performance from Combined Data Sources

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Part of the book series: Studies in Computational Intelligence ((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.

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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

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Acknowledgments

We would like to acknowledge the help and support of JISC and the contribution from Microsoft Research.

Author information

Authors and Affiliations

  1. Knowledge Media Institute, The Open University, Milton Keynes, MK7, 6AA, UK

    Annika Wolff, Zdenek Zdrahal, Drahomira Herrmannova & Petr Knoth

Authors
  1. Annika Wolff
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  2. Zdenek Zdrahal
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  3. Drahomira Herrmannova
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  4. Petr Knoth
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Corresponding author

Correspondence to Annika Wolff .

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Editors and Affiliations

  1. Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco (ESIME-Z), World Outreach Light to the Nations Ministries (WOLNM), Instituto Politécnico Nacional (IPN), Gustavo A. Madero, Mexico City, Distrito Federal, Mexico

    Alejandro Peña-Ayala

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Wolff, A., Zdrahal, Z., Herrmannova, D., Knoth, P. (2014). Predicting Student Performance from Combined Data Sources. In: Peña-Ayala, A. (eds) Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-02738-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-02738-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02737-1

  • Online ISBN: 978-3-319-02738-8

  • eBook Packages: EngineeringEngineering (R0)

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