解决方案之前的问题:大规模的自动问题澄清

S. Basu, A. Wu, Brian Hou, John DeNero
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引用次数: 21

摘要

在大规模的课程中,自动评估减少了对个人反馈的需求,但通常只关注对解决方案的评分,而不是评估学生是否正确理解问题。我们提出了一种丰富的自动评估方法,明确地帮助学生理解他们被要求解决的技术问题的详细说明,以及评估他们的解决方案。学生将获得一套解决方案测试用例,但是他们必须首先通过验证其行为来解锁每个测试用例,然后才允许将其应用于他们提出的解决方案。当在问题解决过程的早期提供这种自动反馈时,学生会提出更少的澄清性问题,并且对评估表达更少的困惑。因此,教师花更少的时间向学生解释问题。在一个1300人的大学课程中,我们观察到绝大多数学生选择在尝试解决问题之前验证他们对测试用例的理解。这些学生报告说,验证过程提高了他们的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Problems Before Solutions: Automated Problem Clarification at Scale
Automatic assessment reduces the need for individual feedback in massive courses, but often focuses only on scoring solutions, rather than assessing whether students correctly understand problems. We present an enriched approach to automatic assessment that explicitly assists students in understanding the detailed specification of technical problems that they are asked to solve, in addition to evaluating their solutions. Students are given a suite of solution test cases, but they must first unlock each test case by validating its behavior before they are allowed to apply it to their proposed solution. When provided with this automated feedback early in the problem-solving process, students ask fewer clarificatory questions and express less confusion about assessments. As a result, instructors spend less time explaining problems to students. In a 1300-person university course, we observed that the vast majority of students chose to validate their understanding of test cases before attempting to solve problems. These students reported that the validation process improved their understanding.
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