A trace-based framework for analyzing and synthesizing educational progressions
A trace-based framework for analyzing and synthesizing educational progressions
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用于分析和综合教育进展的基于轨迹的框架
DOI：
10.1145/2470654.2470764
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发表时间：
2013
期刊：
影响因子：
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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：
Erik Andersen;Sumit Gulwani;Zoran Popovic
教学过程技能的一个关键挑战是找到一个有效的例子问题，学习者可以解决，以内化的过程。在许多学习领域中，这些问题的生成通常是手工完成的，并且很少有工具可以帮助自动化这个过程。我们通过借用软件工程中测试输入生成的思想来减少这种努力。我们展示了如何使用执行痕迹作为一个框架，抽象出一个给定的过程的特点，并定义了一个偏序，反映了两个痕迹的相对难度。我们还展示了如何使用这个框架来分析专家设计的进展的完整性和填补漏洞。此外，我们展示了我们的框架如何通过生成大量的小学和中学数学问题，并为流行的代数学习游戏合成数百个级别来自动合成新问题。我们提出了一个用户研究的结果，这个游戏证实，我们的偏序可以预测用户评价的程序难度比基线方法。
A key challenge in teaching a procedural skill is finding an effective progression of example problems that the learner can solve in order to internalize the procedure. In many learning domains, generation of such problems is typically done by hand and there are few tools to help automate this process. We reduce this effort by borrowing ideas from test input generation in software engineering. We show how we can use execution traces as a framework for abstracting the characteristics of a given procedure and defining a partial ordering that reflects the relative difficulty of two traces. We also show how we can use this framework to analyze the completeness of expert-designed progressions and fill in holes. Furthermore, we demonstrate how our framework can automatically synthesize new problems by generating large sets of problems for elementary and middle school mathematics and synthesizing hundreds of levels for a popular algebra-learning game. We present the results of a user study with this game confirming that our partial ordering can predict user evaluation of procedural difficulty better than baseline methods.