Towards Attention-Based Automatic Misconception Identification in Introductory Programming Courses
Towards Attention-Based Automatic Misconception Identification in Introductory Programming Courses
复制标题
在入门编程课程中实现基于注意力的自动误解识别
DOI：
10.1145/3626253.3635575
复制
发表时间：
2024
期刊：
影响因子：
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通讯作者：
Akram, Bita
中科院分区：
文献类型：
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作者：
Hoq, Muntasir;Vandenberg, Jessica;Mott, Bradford;Lester, James;Norouzi, Narges;Akram, Bita
识别学生编程解决方案中的误解是评估他们对基本编程概念的理解的重要一步。虽然误解是潜在的结构，很难直接从学生的项目中进行评估，但逻辑错误可以表明它们在学生的理解中的存在。追踪不同问题上多次出现的相关逻辑错误可以为学生的误解提供有力的证据。这项研究提出了利用可解释的最先进的基于抽象语法树的嵌入神经网络来识别学生代码中的逻辑错误的初步结果。在这项研究中，我们通过对正确与不正确的程序进行分类，展示了学生程序中发现的错误的概念验证。我们的初步结果表明，我们的框架能够自动识别误解，而无需设计和应用详细的规则。这种方法为教育工作者提供了一个强大的工具，可以提供个性化的反馈，同时能够对学生的误解进行准确的建模，从而有望提高入门编程课程的教学质量。
Identifying misconceptions in student programming solutions is an important step in evaluating their comprehension of fundamental programming concepts. While misconceptions are latent constructs that are hard to evaluate directly from student programs, logical errors can signal their existence in students' understanding. Tracing multiple occurrences of related logical bugs over different problems can provide strong evidence of students' misconceptions. This study presents preliminary results of utilizing an interpretable state-of-the-art Abstract Syntax Tree-based embedding neural network to identify logical mistakes in student code. In this study, we show a proof-of-concept of the errors identified in student programs by classifying correct versus incorrect programs. Our preliminary results show that our framework is able to automatically identify misconceptions without designing and applying a detailed rubric. This approach shows promise for improving the quality of instruction in introductory programming courses by providing educators with a powerful tool that offers personalized feedback while enabling accurate modeling of student misconceptions.
新手程序员常犯的逻辑错误
DOI：
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发表时间：
2018
期刊：
IFAC Symposium on Advances in Control Education
影响因子：
--
作者：
Andrew Ettles;Andrew Luxton;Paul Denny
通讯作者：
Paul Denny