A Procedural Content Generation-Based Framework for Educational Games: Toward a Tailored Data-Driven Game for Developing Early English Reading Skills

Abstract

Automated content generation for educational games has become an emerging research problem, as manual authoring is often time consuming and costly. In this article, we present a procedural content generation framework that intends to produce educational game content from the viewpoint of both designer and user. This framework generates content by means of genetic algorithm, and thereby offers designers the ability to control the process of content generation for various learning goals according to their preferences. It further takes into consideration how the content can adapt according to the skill of the users. We demonstrate effectiveness of the framework by way of an empirical study of human players in an educational language learning game aiming at developing early English reading skills of young children. The results of our study confirm that users' performance measurably improves when game contents are customized to their individual ability, in contrast to their improvement in uncustomized games. Moreover, the results show that the lowest proficiency participants demonstrated greater improvements in performance while playing the customized game than did the more highly proficient participants.