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Design Tools and Data-DrivenMethods to Facilitate Player Authoring in a Programming Puzzle Game

机译：设计工具和数据驱动方法，以促进编程益智游戏中的玩家创作

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摘要

Games-Based Learning systems, particularly those that use advances from Intelligent Tutoring Systems (ITS) to provide adaptive feedback and support, have proven potential as learning tools. Taking their lead from commercial games such as Little Big Planet and SuperMarioMaker, these systems are increasingly turning to content creation as a learning activity and to better engage a broader audience. Existing programming puzzle games such as Light-Bot, COPS and Gidget allow users to build their own puzzles within their games. However, open-ended content creation tools like these do not always provide users with appropriate support. Therefore, player-authors may create content that does not embody the core game mechanics or learning objectives of the game. This wastes the time of both the creator and of any future users who engage with their creations. Better content creation tools are needed to enable users to create effective content for educational games.;A significant barrier to using user-authored problems in learning games is the lack of expert knowledge about the created content.Many Games-Based Learning systems take cues from ITS and use expert-developed contextual hints and content for individualized support and feedback. Data-driven methods exist to generate hints and estimate which skills particular problems may involve, but these methods require sufficient data collection and knowledge engineering to turn prior student work into hints.No prior work has shown that data-driven methods can be used within a programming game. In addition, data for user-generated levels may be very sparse, so evaluation is needed to determine if these methods can be used in this domain.;In this work, I present a set of best practices for designing authoring tools that encourage users to build gameplay affordances into their content. First, I show that requiring users to solve their own levels effectively filters some of the least desirable puzzles, including deliberately impossible or unpleasant levels. Next, I show that the quality of user-authored BOTS puzzles can be improved using level editors designed with these practices. Furthermore, I show that hints and feedback for this content can be created using data-driven methods. I also provide estimates of the amount of data needed to provide adequate hint coverage for a new level using this method. Combined, these findings forman effective framework for integrating user-authored levels into the BOTS game and provide an example of how to build a game from the ground up with user-generated content in mind.;These contributions will help future designers create tools that can effectively guide, filter, and leverage user-generated content that will contain gameplay affordances that support the intended game and learning objectives.
机译：基于游戏的学习系统，尤其是那些利用智能辅导系统（ITS）的先进技术提供自适应反馈和支持的系统，已被证明是学习工具的潜力。这些系统从诸如Little Big Planet和SuperMarioMaker之类的商业游戏中脱颖而出，越来越多地将内容创建作为一种学习活动，以更好地吸引更广泛的受众。诸如Light-Bot，COPS和Gidget之类的现有编程益智游戏允许用户在游戏中构建自己的益智游戏。但是，此类开放式内容创建工具并不总是为用户提供适当的支持。因此，玩家作者可能会创建未体现核心游戏机制或游戏学习目标的内容。这浪费了创作者以及从事其创作的任何未来用户的时间。需要更好的内容创建工具来使用户能够为教育游戏创建有效的内容。；在学习游戏中使用用户创作的问题的一个重大障碍是缺乏对所创建内容的专业知识。许多基于游戏的学习系统都从中汲取了一些线索。 ITS，并使用专家开发的上下文提示和内容进行个性化支持和反馈。存在数据驱动的方法来生成提示并估计特定问题可能涉及的技能，但是这些方法需要足够的数据收集和知识工程以将先前的学生作业转化为提示。编程游戏。此外，用户生成级别的数据可能非常稀疏，因此需要进行评估以确定这些方法是否可以在此领域中使用。；在本工作中，我提出了一组设计创作工具的最佳实践，以鼓励用户将游戏玩法能力纳入其内容。首先，我证明要求用户解决自己的关卡可以有效过滤掉一些最不希望的难题，包括故意造成的不愉快的关卡。接下来，我展示了使用根据这些实践设计的关卡编辑器，可以提高用户编写的BOTS拼图的质量。此外，我展示了可以使用数据驱动的方法来创建有关此内容的提示和反馈。我还提供了使用这种方法为新级别提供足够的提示覆盖率所需的数据量估计值。这些发现共同形成了一个有效的框架，可以将用户授权的关卡集成到BOTS游戏中，并提供一个示例，说明如何从头开始构建一个牢记用户生成内容的游戏。这些贡献将帮助未来的设计师创建可以有效地指导，过滤和利用用户生成的内容，这些内容将包含支持预期游戏和学习目标的游戏玩法能力。

著录项

  • 作者

    Hicks, Andrew Gregory.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Computer science.;Educational technology.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 145 p.
  • 总页数 145
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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