Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC
Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC
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机器学习 MOOC 中百万个代码提交的语法和功能变异性
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
L. Guibas
中科院分区：
文献类型：
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作者：
Jonathan Huang;C. Piech;A. Nguyen;L. Guibas
在斯坦福大学的机器学习大规模开放获取在线课程（MOOC）的第一次提供中，有超过100万的编程提交了42个作业-一系列可能的解决方案的密集样本。在本文中，我们绘制出提交的语法和功能的相似性，以探索解决方案的变化。虽然提交的材料数量众多，但独特的方法却少得多。学生解决方案中的这种冗余可以被用来“强迫倍增”教师反馈。Fig. 1.“线性回归的梯度下降”解决方案的前景代表了超过40，000个学生提交的代码，在语法相似的提交之间绘制了边缘，颜色对应于一组单元测试的性能（红色提交通过了所有单元测试）。
In the first offering of Stanford’s Machine Learning Massive Open-Access Online Course (MOOC) there were over a million programming submissions to 42 assignments — a dense sampling of the range of possible solutions. In this paper we map out the syntax and functional similarity of the submissions in order to explore the variation in solutions. While there was a massive number of submissions, there is a much smaller set of unique approaches. This redundancy in student solutions can be leveraged to “force multiply” teacher feedback. Fig. 1. The landscape of solutions for “gradient descent for linear regression” representing over 40,000 student code submissions with edges drawn between syntactically similar submissions and colors corresponding to performance on a battery of unit tests (red submissions passed all unit tests).