Automated Personalized Assessment of Computational Thinking MOOC Assignments

H. Jamil
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引用次数: 17

Abstract

One of the major hurdles toward automatic semantic understanding of computer programs is the lack of knowledge about what constitutes functional equivalence of code segments. We postulate that a sound knowledgebase can be used to deductively understand code segments in a hierarchical fashion by first de-constructing a code and then reconstructing it from elementary knowledge and equivalence rules of elementary code segments. The approach can also be engineered to produce computable programs from conceptual and abstract algorithms as an inverse function. In this paper, we introduce the core idea behind the MindReader online assessment system that is able to understand a wide variety of elementary algorithms students learn in their entry level programming classes such as Java, C++ and Python. The MindReader system is able to assess student assignments and guide them how to develop correct and better code in real time without human assistance.
计算思维MOOC作业的自动个性化评估
对计算机程序进行自动语义理解的主要障碍之一是缺乏关于代码段的功能等价的知识。我们假设一个健全的知识库可以通过首先对代码进行解构,然后根据基本代码段的基本知识和等价规则对其进行重构,从而以层次方式演绎理解代码段。这种方法也可以被设计成从概念和抽象算法作为逆函数产生可计算的程序。在本文中,我们介绍了MindReader在线评估系统背后的核心思想,该系统能够理解学生在入门级编程课程中学习的各种基本算法,如Java, c++和Python。MindReader系统能够评估学生的作业,并指导他们如何在没有人工帮助的情况下实时开发正确和更好的代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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