The automated grading of student open responses in mathematics
The automated grading of student open responses in mathematics
复制标题
学生数学开放式回答的自动评分
DOI：
10.1145/3375462.3375523
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Heffernan, Neil T.
中科院分区：
文献类型：
--
作者：
Erickson, John A.;Botelho, Anthony F.;McAteer, Steven;Varatharaj, Ashvini;Heffernan, Neil T.
在课堂上使用计算机系统为教师在向学生提供内容、补充教学以及评估学生的知识和理解方面提供了新的机会。这些系统的最大好处之一是，它们能够向学生提供对他们工作的反馈，并向他们的老师报告学生的表现和进步。虽然基于计算机的系统可以自动评估学生对一系列问题类型的答案，但许多系统面临的一个限制是关于开放式问题。许多系统要么无法为开放式问题提供支持，依赖教师手动评分，要么完全避免这种问题类型。由于最近自然语言处理方法的进步，作文评分的自动化已经取得了显著的进展。然而，这项研究的大部分涉及数学以外的领域，教师可以使用开放式问题来评估学生对数学概念的理解程度，而不是对其他类型的问题的理解。这项研究探索了开发开放式学生数学反应自动评分器的可行性和挑战。我们将进一步探讨可用数据的规模如何影响模型性能。聚焦于通过ASSISTments在线学习平台提供的内容，我们提出了一套与模型的开发和评估有关的分析，以预测学生开放回答的教师分配的分数。
The use of computer-based systems in classrooms has provided teachers with new opportunities in delivering content to students, supplementing instruction, and assessing student knowledge and comprehension. Among the largest benefits of these systems is their ability to provide students with feedback on their work and also report student performance and progress to their teacher. While computer-based systems can automatically assess student answers to a range of question types, a limitation faced by many systems is in regard to open-ended problems. Many systems are either unable to provide support for open-ended problems, relying on the teacher to grade them manually, or avoid such question types entirely. Due to recent advancements in natural language processing methods, the automation of essay grading has made notable strides. However, much of this research has pertained to domains outside of mathematics, where the use of open-ended problems can be used by teachers to assess students' understanding of mathematical concepts beyond what is possible on other types of problems. This research explores the viability and challenges of developing automated graders of open-ended student responses in mathematics. We further explore how the scale of available data impacts model performance. Focusing on content delivered through the ASSISTments online learning platform, we present a set of analyses pertaining to the development and evaluation of models to predict teacher-assigned grades for student open responses.
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