用大型语言模型生成的学生学习习惯和偏好的全合成文本数据集
1Faculty of Computer Science and Engineering, Patuakhali Science and Technology University, Patuakhali 8602, Bangladesh.
Data in brief
|February 24, 2026
概括
使用大型语言模型 (LLM) 创建了一个学生学习习惯的新合成数据集. 这种保护隐私的数据集非常适合用于教育自然语言处理 (NLP) 工具的基准测试.
科学领域:
- 教育数据挖掘教育数据挖掘
- 学习分析学习分析
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 教育NLP的进步需要结构化的文本数据集.
- 由于隐私和道德问题,对真实学生数据的访问受到限制.
- 合成数据为研究提供了一个保护隐私的替代方案.
研究的目的:
- 为学生学习习惯和偏好提供完全合成的文本数据集.
- 为保护隐私的教育研究和NLP开发提供资源.
- 为了促进教育NLP管道和合成数据生成技术的基准测试.
主要方法:
- 使用大型语言模型 (LLM) 生成合成数据集.
- 利用结构化提示策略和控制的词汇来生成数据.
- 创建了1万个CSV格式的记录与虚构的学生属性.
主要成果:
- 成功生成了一个由10,000个虚构学生记录组成的合成数据集.
- 数据集包括学习习惯,学习偏好和挑战等属性.
- 数据展示了受控的分布,适合特定的基准测试任务.
结论:
- 合成数据集使得在教育数据挖掘和NLP方面进行保护隐私的研究成为可能.
- 它是评估NLP管道和合成数据方法的宝贵工具.
- 促进可重现的研究,而不影响学生的隐私.
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