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Educational Data Mining pp 151–174Cite as

Students’ Performance Prediction Using Multi-Channel Decision Fusion

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 524))

Abstract

A teacher or an artificial instructor, embedded in an intelligent tutoring system, is interested in predicting the performance of his/her students to better adjust the educational materials and strategies throughout the learning process. In this chapter, a multi-channel decision fusion approach, based on using the performance in “assignment categories”, such as homework assignments, is introduced to determine the overall performance of a student. In the proposed approach, the data gathered are used to determine four classes of “expert”, “good”, “average”, and “weak” performance levels. This classification is conducted on both overall performance and the performance in assignment categories. Then, a mapping from the performances in “assignment categories” is learned, and is used to predict the overall performance. The main advantage of the proposed approach is in its capability to estimate students’ performance after a few assignments. Consequently, it can help the instructors better manage their class and adjust educational materials to prevent underachievement.

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Notes

  1. 1.

    At-risk students are the ones who do not experience success at school, more likely to fail academically, and may drop out. It is important to detect them and help them as early as possible to avoid future failures and eventual drop out.

  2. 2.

    Also referred as the mixed methods.

  3. 3.

    A survey was performed to validate this observation which has been explained at the end of Section 6.5.

  4. 4.

    http://moss.stanford.edu.

  5. 5.

    Waikato Environment for Knowledge Analysis.

Abbreviations

BKT:

Bayesian knowledge tracing

CART:

Classification and regression tree

CHAID:

Chi-square automatic interaction detection

EDM:

Educational data mining

ITS:

Intelligent tutoring systems

LMS:

Learning management system

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Acknowledgements

The authors like to thank Babak Araabi and Maryam Mirian for their feedback on this chapter. Furthermore, the authors like to thank the ITS group at the Advanced Robotics and Intelligent Systems Laboratory for their help throughout this research.

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Authors and Affiliations

  1. School of ECE, University of Tehran, North Kargar St, P.O. Box 14395-515, Tehran, Iran

    H. Moradi & S. Abbas Moradi

  2. Department of Psychology and Special Education, University of Tehran, Jalal Al-e-Ahmad Avenue, P.O. Box 11455/6456, Tehran, Iran

    L. Kashani

Authors
  1. H. Moradi
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  2. S. Abbas Moradi
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  3. L. Kashani
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Correspondence to H. Moradi .

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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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Moradi, H., Moradi, S.A., Kashani, L. (2014). Students’ Performance Prediction Using Multi-Channel Decision Fusion. 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_6

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  • DOI: https://doi.org/10.1007/978-3-319-02738-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02737-1

  • Online ISBN: 978-3-319-02738-8

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