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A Data-Driven Procedural-Content-Generation Approach for Educational Games

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Title: A Data-Driven Procedural-Content-Generation Approach for Educational Games
Language: English
Authors: Hooshyar, Danial (ORCID 0000-0002-9143-6648), Yousefi, M., Wang, M., Lim, H.
Source: Journal of Computer Assisted Learning. Dec 2018 34(6):731-739.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 9
Publication Date: 2018
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Educational Games, Computer Games, Data, Individualized Instruction, Student Needs, Instructional Effectiveness, Instructional Design, Second Language Instruction, Second Language Learning, English (Second Language), Reading Skills, Preschool Children, Foreign Countries, Teaching Methods, Models, Reading Instruction
Geographic Terms: South Korea (Seoul)
DOI: 10.1111/jcal.12280
ISSN: 0266-4909
Abstract: Although game-based learning has been increasingly promoted in education, there is a need to adapt game content to individual needs for personalized learning. Procedural content generation (PCG) offers a solution for difficulty in developing game contents automatically by algorithmic means as it can generate individually customizable game contents applicable to various objectives. In this paper, we advanced a data-driven PCG approach benefiting from a genetic algorithm and support vector machines to automatically generate educational-game contents tailored to individuals' abilities. In contrast to other content generation approaches, the proposed method is not dependent on designer's intuition in applying game contents to fit a player's abilities. We assessed this data-driven PCG approach at length and showed its effectiveness by conducting an empirical study of children who played an educational language-learning game to cultivate early English-reading skills. To affirm the efficacy of our proposed method, we evaluated the data-driven approach against a heuristic-based approach. Our results clearly demonstrated two things. First, users realized greater performance gains from playing contents tailored to their abilities compared with playing uncustomized game contents. Second, this data-driven approach was more effective in generating contents closely matching a specific player-performance target than the heuristic-based approach.
Abstractor: As Provided
Entry Date: 2018
Accession Number: EJ1196165
Database: ERIC
FullText Text:
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PubType: Academic Journal
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  Data: A Data-Driven Procedural-Content-Generation Approach for Educational Games
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  Data: <searchLink fieldCode="AR" term="%22Hooshyar%2C+Danial%22">Hooshyar, Danial</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-9143-6648">0000-0002-9143-6648</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yousefi%2C+M%2E%22">Yousefi, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+M%2E%22">Wang, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Lim%2C+H%2E%22">Lim, H.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. Dec 2018 34(6):731-739.
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  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
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  Data: Y
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  Data: 9
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  Data: Journal Articles<br />Reports - Descriptive
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  Data: <searchLink fieldCode="DE" term="%22Educational+Games%22">Educational Games</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Games%22">Computer Games</searchLink><br /><searchLink fieldCode="DE" term="%22Data%22">Data</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+Instruction%22">Individualized Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Needs%22">Student Needs</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Skills%22">Reading Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Preschool+Children%22">Preschool Children</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Instruction%22">Reading Instruction</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22South+Korea+%28Seoul%29%22">South Korea (Seoul)</searchLink>
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  Data: 10.1111/jcal.12280
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  Data: 0266-4909
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Although game-based learning has been increasingly promoted in education, there is a need to adapt game content to individual needs for personalized learning. Procedural content generation (PCG) offers a solution for difficulty in developing game contents automatically by algorithmic means as it can generate individually customizable game contents applicable to various objectives. In this paper, we advanced a data-driven PCG approach benefiting from a genetic algorithm and support vector machines to automatically generate educational-game contents tailored to individuals' abilities. In contrast to other content generation approaches, the proposed method is not dependent on designer's intuition in applying game contents to fit a player's abilities. We assessed this data-driven PCG approach at length and showed its effectiveness by conducting an empirical study of children who played an educational language-learning game to cultivate early English-reading skills. To affirm the efficacy of our proposed method, we evaluated the data-driven approach against a heuristic-based approach. Our results clearly demonstrated two things. First, users realized greater performance gains from playing contents tailored to their abilities compared with playing uncustomized game contents. Second, this data-driven approach was more effective in generating contents closely matching a specific player-performance target than the heuristic-based approach.
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  Data: EJ1196165
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      – SubjectFull: Educational Games
        Type: general
      – SubjectFull: Computer Games
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      – SubjectFull: Data
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      – SubjectFull: Individualized Instruction
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