Generating Object Placements for Optimum Exploration and Unpredictability in Medium-Coupling Educational Games

Educational games are powerful tools in the era of Education 4.0. However, their applications are hampered by many issues, including high development costs. Procedural content generation (PCG) and medium coupling are two potent methods for reducing an educational game's development cost; however, their joint application is under-studied. In this study, we explored the topic through a PCG application for generating placements of objects representing elements of learning content in a game map. The content consisted of correct answers, which the player must gather in the correct order, and wrong answers, which the player must avoid. We employed an evolutionary algorithm in three stages of the generation: it first generated a minimal set of correct answer element objects (CAEOs), followed by generating a copy of the set, and then a set of wrong answer element objects (WAEOs). We employed and tested a fitness function that calculated the mean and standard deviation of object pair distances. The results show that the generation algorithm is capable of generating CAEOs and WAEOs distributions that are unpredictable and encourage exploration. We find that multiplying the fitness function's standard deviation variable by a specific value, which depends on how many objects are to be generated, is crucial to the algorithm's success. We also discuss the limitations of this study and directions for future ones.