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 languagelearning 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. KEYWORDS data-driven approach, early English-reading skills, educational game, procedural contents generation 1 | INTRODUCTION Adaptive learning is an educational method for personalized learning. Personalized learning refers to learning and instructional approaches that are driven by needs and interests of individual learners. It represents a shift from teacher-centred learning towards student-centred learning. It is built upon constructivist learning theory that emphasizes the critical role of student in learning by constructing personally relevant meaning. Personalized learning cannot be realized in traditional classrooms in a large scale. Instead, computers or information and communication technologies play an important role in this regard by the following: (a) adapting learning objectives, content, and progress according to individual learning needs, abilities, prior knowledge, and preferences; (b) recording individual activities for analysis; and (c) providing personalized feedback after assessing individual performance.