Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of such crowd-sourced questions, quantifying the properties of these questions in crowd-sourced online education platforms is of great importance to enable both teachers and students to find high-quality and suitable resources. In this work, we propose a framework for large-scale question analysis. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders, to analyze real-world educational data. We also develop novel objectives to quantify question quality and difficulty. We apply our proposed framework to a real-world cohort with millions of question-answer pairs from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain expert evaluation.

中文翻译：

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使用部分变分自动编码器进行大规模教育问题分析

在线教育平台使教师可以共享大量的教育资源，例如问题，为学生形成练习和测验。由于此类众包问题的数量庞大，在众包在线教育平台中量化这些问题的属性对于教师和学生都能找到高质量和合适的资源具有重要意义。在这项工作中，我们提出了一个用于大规模问题分析的框架。我们利用最先进的贝叶斯深度学习方法，特别是部分变分自动编码器，来分析现实世界的教育数据。我们还开发了新的目标来量化问题的质量和难度。我们将我们提出的框架应用于现实世界的队列，该队列具有来自在线教育平台的数百万对问答对。