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Abstractive Analysis of Traditional and GPT-based Methods for Solving Algebra Problems

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成果类型:
期刊论文
作者:
Jing Xia;Xinguo Yu
作者机构:
[Xinguo Yu] National Engineering Research Center for E-Learning, Central China Normal University, China
[Jing Xia] College of International Cultural Exchange, Central China Normal University, China
语种:
英文
关键词:
Abstractive Analysis;Algorithm;Approach;GPT;Solving Algebra Problem;State-Transit Scaffold
年:
2023
页码:
101–105
机构署名:
本校为第一机构
院系归属:
国际文化交流学院
国家数字化学习工程技术研究中心
摘要:
GPT has made the noticeable impact on research of solving algebra problems. In order to fuse the good features of GPT with the traditional methods to design better algorithms, this paper analyzes the approaches of solving algebra problems to understand their abstractive features. To this end, this paper classifies the approaches by means of state-transit analysis and then reveals their abstractive features such as assumptions and scopes from the algorithm descriptions. It further analyzes their application-related features such as readability. The tables are built to compare the features of th...

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