Hao Fa Yin: Developing Automated Audio Assessment Tools for a Chinese Language Course
Hao Fa Yin: Developing Automated Audio Assessment Tools for a Chinese Language Course
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尹浩发：为汉语课程开发自动音频评估工具
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
N. Heffernan
中科院分区：
文献类型：
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作者：
A. Varatharaj;Anthony F. Botelho;Xiwen Lu;N. Heffernan
我们提出并评估了一个基于机器学习的系统，该系统可以自动对学生说外语的音频进行评分。使用自动化系统来帮助评估学生的表现，有望增强教师向学生提供有意义的反馈和指导的能力。教师花费大量时间对学生作业进行评分，使用这些工具可以为教师节省大量评分时间。这些额外的时间可以用来对每个学生给予个性化的关注。先前的重要研究集中在封闭式问题、开放式论文和文本内容的评分上。然而，很少有研究关注在语言学习教育中更为普遍的音频内容。在本文中，我们探索了大学汉语学习课程中音频响应自动评估工具的开发。我们分析了处理此类数据以及特征的生成和提取时面临的几个挑战，以构建机器学习模型来帮助评估学生的语言学习。
We present and evaluate a machine learning based system that automatically grades audios of students speaking a foreign language. The use of automated systems to aid the assessment of student performance holds great promise in augmenting the teacher’s ability to provide meaningful feedback and instruction to students. Teachers spend a significant amount of time grading student work and the use of these tools can save teachers a significant amount of time on their grading. This additional time could be used to give personalized attention to each student. Significant prior research has focused on the grading of closed-form problems, open-ended essays and textual content. However, little research has focused on audio content that is much more prevalent in the language-study education. In this paper, we explore the development of automated assessment tools for audio responses in a college-level Chinese language-learning course. We analyze several challenges faced while working with data of this type as well as the generation and extraction of features for the purpose of building machine learning models to aid in the assessment of student language learning.