探索设计一个自动分类器的问题包含代码片段-一个案例研究的Oracle SQL认证考试问题
Hung-Yi Chen1, Po-Chou Shih2, Yunsen Wang3
1Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan.
PloS one
|January 9, 2025
概括
这项研究开发了Oracle SQL考试问题与代码的自动分类器. 在问题分类方面,TF-IDF方案和物流回归模型取得了最佳表现.
科学领域:
- 计算机科学 计算机科学
- 教育技术的教育技术
背景情况:
- 考试题的自动分类有助于测试组装.
- 与纯文本问题相比,使用代码片段对SQL问题进行分类具有独特的挑战.
研究的目的:
- 设计和评估包含代码片段的Oracle SQL认证考试问题的自动分类器.
- 为此分类任务确定最有效的特征表示方案和机器学习方法.
主要方法:
- 利用因数实验来分析特征表示 (例如,TF-IDF) 和机器学习模型 (例如,物流回归,支持矢量机器) 的影响.
- 使用加权宏平均AUC,F1和精度指标评估分类器性能.
主要成果:
- TF-IDF特征表示方案与物流回归模型相结合,在AUC和F1得分方面产生了最佳结果.
- 支持矢量机的TF-IDF在加权宏观平均精度方面表现出色.
- 特性表示方案被确定为影响分类器性能的主要因素.
结论:
- 该TF-IDF方案是高效的对分类Oracle SQL考试问题与代码.
- 物流回归和支持向量机器是这个应用程序的可行的机器学习模型.
- 特征表示显著影响自动问题分类系统的成功.
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