Automatic Adaptive Sequencing in a Webgame
Automatic Adaptive Sequencing in a Webgame
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网页游戏中的自动自适应排序
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
E. Brunskill
中科院分区：
文献类型：
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作者：
Tong Mu;Shuhan Wang;Erik Andersen;E. Brunskill
。智能辅导系统可以提高学生的成绩，但开发此类系统通常需要使用该系统的学生的丰富专业知识或先前数据。在这项工作中，我们提出了一种新方法，可以自动自适应地对单个学生的练习活动进行排序。我们的方法建立在自动构建课程图表和使用多臂 ban-dit 算法通过图表提升学生进步的基础上。这些方法具有相对较少的超参数，并且被设计为在有限或没有先验数据的情况下运行良好。我们在基础韩语学习的在线游戏中评估了我们的方法，该方法可以应用于多种领域，并发现了有希望的初步结果。与专家设计的固定排序相比，我们的自适应算法对游戏性能中定义的学习效率指标具有统计上显着的积极影响。
. Intelligent tutoring systems can improve student outcomes, but developing such systems typically requires significant expertise or prior data of students using the system. In this work we propose a new approach for automatically adaptively sequencing practice activities for an individual student. Our approach builds on progress for automatically constructing curriculum graphs and advancing a student through a graph using a multi-armed ban-dit algorithm. These approaches have relatively few hyperparameters and are designed to work well given limited or no prior data. We evaluate our method, which can be applied to a diverse range of domains, in our online game for basic Korean language learning and found promising initial results. Compared to an expert-designed fixed ordering, our adaptive algorithm had a statistically significant positive effect on a learning efficiency metric defined using in game performance.