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Using Data Mining Techniques to Detect the Personality of Players in an Educational Game

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Educational Data Mining

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

Abstract

One of the goals of Educational Data Mining is to develop the methods for student modeling based on educational data, such as; chat conversation, class discussion, etc. On the other hand, individual behavior and personality play a major role in Intelligent Tutoring Systems (ITS) and Educational Data Mining (EDM). Thus, to develop a user adaptable system, the student’s behaviors that occurring during interaction has huge impact EDM and ITS. In this chapter, we introduce a novel data mining techniques and natural language processing approaches for automated detection student’s personality and behaviors in an educational game (Land Science) where students act as interns in an urban planning firm and discuss in groups their ideas. In order to apply this framework, input excerpts must be classified into one of six possible personality classes. We applied this personality classification method using machine learning algorithms, such as: Naive Bayes, Support Vector Machine (SVM) and Decision Tree.

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Abbreviations

CBLE:

Computer based learning environment

CRF:

Conditional random field

EDM:

Educational data mining

ITS:

Intelligent tutoring system

LIWC:

Linguistic inquiry and word count

NPC:

Non-player characters

SVM:

Support vector machine

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Acknowledgments

This work was funded by the National Science Foundation (DRK-12-0918409). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these funding agencies, cooperating institutions, or other individuals.

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Authors and Affiliations

  1. Southeast Missouri State University, DH 021F, Mail Stop, 5950, USA

    Fazel Keshtkar, Candice Burkett, Haiying Li & Arthur C. Graesser

Authors
  1. Fazel Keshtkar
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  2. Candice Burkett
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  3. Haiying Li
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  4. Arthur C. Graesser
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Correspondence to Fazel Keshtkar .

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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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Keshtkar, F., Burkett, C., Li, H., Graesser, A.C. (2014). Using Data Mining Techniques to Detect the Personality of Players in an Educational Game. 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_5

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  • DOI: https://doi.org/10.1007/978-3-319-02738-8_5

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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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