EVENLY MATCHED COMPETITIVE STRATEGIES: DYNAMIC DIFFICULTY ADAPTATION IN A GAME-BASED LEARNING SYSTEM

Abstract

Game-based learning is a highly motivational learning approach, with appropriate difficulty level being the key to level of motivation in this type of learning. However, it is not easy to adapt the difficulty of game-based learning for some students. This study proposes two evenly matched competitive strategies to dynamically adapt the difficulty of game-based learning during the game, while matching game progress and maintaining evenly matched game results. The strategies are designed to realize a even opportunity tactic to manipulate perceived performance in game-based learning. This study also proposes three adaptation methods: Adjusting the complexity of learning tasks, uncertain game factors, and virtual characters to realize the strategies. A system was implemented and two preliminary experiments were conducted with a total of 56 participants to validate the strategies and adaptations. The results of the experiments show that adaptations based on strategies can dynamically adjust in order for different students to keep the game evenly matched.