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调查线性代数中的困难并增强对线性代数的理解:利用SageMath和ChatGPT进行 (直角) 对角化和奇数值分解
1Department of Mathematics, University College, Natural Science Campus, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
这项研究调查了本科线性代数的挑战,使用SageMath进行计算和ChatGPT进行问题解决. 这两种工具都有助于掌握矩阵因子化主题,如对角化和SVD.
科学领域:
- 数学 数学 是一个数学.
- 计算机科学 计算机科学
- 教育教育教育教育教育教育.
背景情况:
- 本科生经常在线性代数中的算法思维中扎,特别是在矩阵分解技术中.
- 像直角对角化和奇数值分解 (SVD) 这样的话题存在重大概念和计算障碍.
研究的目的:
- 探索SageMath和ChatGPT在支持本科线性代数教育中的实用性.
- 提高学生对矩阵分解,对角化和SVD的理解和计算技能.
主要方法:
- 使用SageMath,一个基于Python的开源计算机代数系统,用于线性代数中的计算任务.
- 采用动态ChatGPT,一个AI聊天机器人,生成示例并提供用于对角化和SVD的解决问题的帮助.
主要成果:
- 尽管它具有静态输出,但SageMath由于其清晰的语法和准确性,有效地确认了计算,并管理了复杂的计算.
- 聊天GPT,虽然不总是可靠的直接解决问题,提供了宝贵的学习机会通过它的错误,培养批判性思维.
结论:
- SageMath作为一个强大的工具来验证线性代数计算和掌握繁的计算.
- 即使有不准确性,ChatGPT的动态性质也可以被利用来培养学生在线性代数中的批判性思维和问题解决策略.
关键词:
(直角) 对角化的对角化基于人工智能的聊天机器人聊天GPT 聊天 在GPT 聊天聪明的数学 聪明的数学算法思维技巧 算法思维技巧计算机代数系统计算机代数系统线性代数的线性代数.单一价值分解分解的方法更多相关视频
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