计算机科学
可读性
人工智能
深度学习
背景(考古学)
同步学习
教育技术
机器学习
自然语言处理
多媒体
万维网
教学方法
数学教育
合作学习
心理学
生物
古生物学
程序设计语言
作者
Hanxiang Du,Wanli Xing,Bo Pei
标识
DOI:10.1080/10494820.2021.1993932
摘要
Participating in online communities has significant benefits to students learning in terms of students' motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to explore the use of deep learning algorithms for automatic text generation in providing emotional and community support for a massive online learning community, Scratch. Particularly, state-of-art deep learning language models GPT-2 and recurrent neural network (RNN) are trained using two million comments from the online learning community. We then conduct both a readability test and human evaluation on the automatically generated results for offering support to the online students. The results show that the GPT-2 language model can provide timely and human-written like replies in a style genuine to the data set and context for offering related support.
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