Modeling Player Knowledge in a Parallel Programming Educational Game
Modeling Player Knowledge in a Parallel Programming Educational Game
复制标题
在并行编程教育游戏中对玩家知识进行建模
DOI：
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发表时间：
2020
影响因子：
2.3
通讯作者：
Santiago Ontañón
中科院分区：
文献类型：
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作者：
Pavan Kantharaju;K. Alderfer;Jichen Zhu;B. Char;Brian Smith;Santiago Ontañón
这篇文章的重点是在教育游戏中追踪玩家的知识。具体来说，给定掌握游戏所需的一组概念或技能，目标是估计当前玩家掌握这些概念或技能中的每一个的可能性。这项工作的主要贡献是一种集成机器学习和领域知识规则的方法，以发现玩家何时应用了某种技能并成功或失败。然后将其作为输入提供给标准知识跟踪模块（例如来自智能辅导系统的那些模块）以执行知识跟踪。我们评估我们的方法在一个名为并行的教育游戏的背景下，教并行和并发编程从真实的用户收集的数据，显示我们的方法可以预测学生的技能，具有较低的均方误差。我们还提供了我们的系统在教室环境中部署的结果。
This article focuses on tracing player knowledge in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the work is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from intelligent tutoring systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called Parallel to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error. We also provide results from deployment of our system in a classroom environment.