关于教育中的问题生成研究的文献综述
Xiaohui Dong1, Xinyu Zhang1, Zhengluo Li1
1College of Computer Science & Engineering, Northwest Normal University, Lanzhou, Gansu, China.
PeerJ. Computer science
|September 24, 2025
概括
本综述探讨了教育中的问题生成 (QGEd),这是一个关键的自然语言处理任务. 它详细介绍了个性化学习技术的进展,当前的研究和未来的方向.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 教育技术的教育技术
背景情况:
- 问题生成 (QG) 对于提高教育和实现个性化学习至关重要.
- 教育中问题生成 (QGEd) 的领域已经发生了显著的发展,需要进行全面的审查.
研究的目的:
- 提供对QGEd.研究进展和未来方向的深入审查.
- 系统地分析QGEd研究格局,确定挑战和机遇.
主要方法:
- 追踪QG和QGEd. 的历史演变.
- 探索跨目标,数据集和评估方法的当前QGEd研究.
- 进行研究环境的系统分析.
主要成果:
- 目前的QGEd研究重点是核心目标,数据集和评估指标.
- 已经确定了QGEd的关键挑战和机遇.
- 未来的研究需要关注多式联运数据,可控性,数据集,评估和架构.
结论:
- QGEd是推动教育技术发展的关键领域.
- 未来的研究应该优先考虑多模式处理,认知/难度控制,专用数据集,自动评估和系统设计.
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