Goal-based progression synthesis in a korean learning game

Designing educational games is difficult, and ideally designers should be able to rely on tools that take some of the burden off them by generating content automatically. Previous work in automatic level generation and sequencing for educational games has primarily focused on achieving a gradual increase of difficulty. However, engagement often comes from a sense of accomplishment after completing hard tasks [27]. For this reason, many games feature "boss levels" that are more intense. In this paper, we propose that a good progression should be goal-based, meaning that it should build up towards some hard tasks (goals) as soon as possible to create a sense of satisfaction. To achieve this, we propose a graph-based algorithm for automatically synthesizing goal-based learning progressions of user-specific length. We also introduce Katchi, a Korean language learning puzzle game that is designed to be highly parameterizable, to evaluate our synthesized progression. In an evaluation of a Korean learning game with 248 participants, our synthesized progression performed similarly to an expert-designed progression in terms of both our engagement and learning metrics, demonstrating that our algorithm is capable of automatically synthesizing goal-based progressions that are comparable to the manually created progressions.

设计教育游戏是困难的，理想情况下，设计师应该能够依靠一些工具，通过自动生成内容来减轻他们的一些负担。先前在教育游戏的自动关卡生成和排序方面的工作主要集中在实现难度的逐步增加。然而，参与度往往来自于完成困难任务后的成就感[27]。出于这个原因，许多游戏都设有更具挑战性的“头目关卡”。在本文中，我们提出一个良好的进程应该基于目标，这意味着它应该尽快朝着一些困难任务（目标）推进，以创造一种满足感。为了实现这一点，我们提出了一种基于图的算法，用于自动合成特定用户长度的基于目标的学习进程。我们还介绍了Katchi，一款韩语学习益智游戏，它被设计为高度可参数化，用于评估我们合成的进程。在对一款有248名参与者的韩语学习游戏的评估中，我们合成的进程在参与度和学习指标方面与专家设计的进程表现相似，这表明我们的算法能够自动合成与手动创建的进程相当的基于目标的进程。