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

Autor/inn/en	Hooshyar, Danial; Yousefi, M.; Wang, M.; Lim, H.
Titel	A Data-Driven Procedural-Content-Generation Approach for Educational Games
Quelle	In: Journal of Computer Assisted Learning, 34 (2018) 6, S.731-739 (9 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Hooshyar, Danial)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0266-4909
DOI	10.1111/jcal.12280
Schlagwörter	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; South Korea (Seoul) + Suchen Sie Ihr Suchwort? Educational game; Lernspiel; Computer game; Computerspiel; Computerspiele; Daten; Individualisierender Unterricht; Unterrichtserfolg; Lesson concept; Lessonplan; Unterrichtsentwurf; Fremdsprachenunterricht; Zweitsprachenerwerb; English as second language; English; Second Language; Englisch als Zweitsprache; Reading skill; Lesefertigkeit; Pre-school age; Preschool age; Child; Children; Pre-school education; Preschool education; Vorschulalter; Kind; Kinder; Vorschulkind; Vorschulkinder; Vorschulerziehung; Vorschule; Ausland; Teaching method; Lehrmethode; Unterrichtsmethode; Analogiemodell; Leseunterricht
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. (As Provided).
Anmerkungen	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
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01