A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations
A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations
复制标题
针对异质学生群体的自动教学策略比较
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. Lopes
中科院分区：
文献类型：
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作者：
B. Clément;Pierre;M. Lopes
在线规划好的教学顺序有可能提供真正个性化的教学体验，对学生的动机和学习产生巨大影响。在这项工作中，我们比较了实现这一目标的两种主要方法，一种是可以找到最佳长期路径的pomdp，另一种是局部和贪婪地优化策略的多武装强盗，但它在计算上更高效，同时需要更简单的学习器模型。即使有来自几个辅导系统的可用数据，也不可能有一个高度准确的学生模型，或者为每个特定的学生进行调整。我们研究了学生模型的质量对两种算法获得的最终结果的影响。我们的假设是，就学生模型的复杂性和精度而言，多臂强盗的更高灵活性将弥补pomdp中缺乏长期规划的特点。我们提出了几个模拟结果，显示了每种方法的局限性和鲁棒性，并对异质学生群体进行了比较。
Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally and greedily but that are computationally more efficient while requiring a simpler learner model. Even with the availability of data from several tutoring systems, it is never possible to have a highly accurate student model or one that is tuned for each particular student. We study what is the impact of the quality of the student model on the final results obtained with the two algorithms. Our hypothesis is that the higher flexibility of multi-armed bandits in terms of the complexity and precision of the student model will compensate for the lack of longer term planning featured in POMDPs. We present several simulated results showing the limits and robustness of each approach and a comparison of heterogeneous populations of students.