Using Data Mining Techniques to Detect the Personality of Players in an Educational Game
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 Intelligen
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Using Data Mining Techniques to Detect the Personality of Players in an Educational Game Fazel Keshtkar, Candice Burkett, Haiying Li and Arthur C. Graesser
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.
Keywords Personality Classification Conversation Larry’s Rose framework Natural language processing Educational data
F. Keshtkar (&) C. Burkett H. Li A. C. Graesser Southeast Missouri State University, DH 021F, Mail Stop 5950, USA e-mail: [email protected] C. Burkett e-mail: [email protected] H. Li e-mail: [email protected] A. C. Graesser e-mail: [email protected]
A. Peña-Ayala (ed.), Educational Data Mining, Studies in Computational Intelligence 524, DOI: 10.1007/978-3-319-02738-8_5, Springer International Publishing Switzerland 2014
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Abbreviations CBLE CRF EDM ITS LIWC NPC SVM
Computer based learning environment Conditional random field Educational data mining Intelligent tutoring system Linguistic inquiry and word count Non-player characters Support vector machine
5.1 Introduction Interpersonal conversation is not an easy task. During conversation in educational games, ITS, or chat interaction, the students may have different ideas from the others. Because they may affect by different moods or personality when they listen or say something. On the other hand, students might have different personality characters, i.e., to be cooperative, leading, aggressive, or dependent. For all these reason, we believe personality traits should be considered in computer-based learning environments (CBLE) such as educational game and intelligent tutoring systems. For example, attitudes toward computers can be related to personality types such that those displaying higher scores on neuroticism may have greater computer related anxiety. Furthermore, it is known that it is important to take individual differences into account during learning in CBLE. For example, ITS are known for their ability to simulate effective human tutoring methods as well as take into account the individual needs of learners [1]. Although the
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