Towards automatic experimentation of educational knowledge
Towards automatic experimentation of educational knowledge
复制标题
走向教育知识的自动实验
DOI：
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发表时间：
2014
期刊：
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通讯作者：
Zoran Popovic
中科院分区：
文献类型：
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作者：
Yun;Travis Mandel;E. Brunskill;Zoran Popovic
我们提出了一个通用的自动实验和假设生成框架，利用大量的用户来探索干预参数空间的不同部分对任何目标函数的影响。我们还结合了重要性抽样，即使我们不能给出我们想要的确切干预分布，也可以运行这些自动实验。为了展示这个框架的实用性，我们提出了一个实现领域的分数和数字线，使用在线教育游戏作为源的球员。我们的系统能够自动探索参数空间，并生成关于什么类型的数字线导致最大短期转移的假设;在单独的数据集上进行测试，表明最有希望的假设是有效的。我们简要地讨论了我们的研究结果的背景下，更广泛的教育文献，表明我们的研究结果之一是不能解释目前的研究多个分数表示，从而证明我们有能力产生潜在的有趣的假设进行测试。
We present a general automatic experimentation and hypothesis generation framework that utilizes a large set of users to explore the effects of different parts of an intervention parameter space on any objective function. We also incorporate importance sampling, allowing us to run these automatic experiments even if we cannot give out the exact intervention distributions that we want. To show the utility of this framework, we present an implementation in the domain of fractions and numberlines, using an online educational game as the source of players. Our system is able to automatically explore the parameter space and generate hypotheses about what types of numberlines lead to maximal short-term transfer; testing on a separate dataset shows the most promising hypotheses are valid. We briefly discuss our results in the context of the wider educational literature, showing that one of our results is not explained by current research on multiple fraction representations, thus proving our ability to generate potentially interesting hypotheses to test.