一个问题答案生成系统,用于异步远程学习平台
Hei-Chia Wang1,2, Martinus Maslim1,3, Chia-Hao Kan1
1Institute of Information Management, College of Management, National Cheng Kung University, Tainan City, Taiwan.
概括
本研究介绍了一种用于异步远程学习的自动化问题生成模型. 异步远程教学问题生成 (ADT-QG) 模型增强了学生的参与度,并有助于在线教育中对教师的评估.
科学领域:
- 教育技术的教育技术
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 远程学习提供了灵活性,但面临着参与挑战,特别是在异步格式.
- 异步学习限制了学生与老师的互动,阻碍了理解的评估.
- 自动化问题生成可以弥合这一差距,改善参与和评估.
研究的目的:
- 开发一个自动化系统,从异步学习材料生成问题.
- 创建多选题,以方便学生回答和教师评分.
- 提高异步远程教育的有效性.
主要方法:
- 提出了异步远程教学问题生成 (ADT-QG) 模型.
- 综合句子-BERT (SBERT) 用于高相似性句子到问题生成.
- 利用转移文本到文本转换器 (T5) 模型与维基语料库用于流和相关的问题生成.
主要成果:
- ADT-QG模型在生成问题时表现出良好的流性和清晰度.
- 生成的问题显示与教学内容的相关性.
- 该模型有效地解决了在异步学习中参与和评估的需求.
结论:
- ADT-QG模型是改善异步远程学习的一个有希望的工具.
- 自动化问题生成可以显著提高学生的理解能力和教师的反.
- 这种方法支持更有效的在线教育体验.
关键词:
远程学习 远程学习问题生成问题生成句子 - 伯特 (SBERT) 的时间T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T5 T1 T1 T1 T1 T1 T2 T1 T1 T2 T1 T1 T1 T2 T1 T1 T2 T1 T1相关概念视频
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