Automatic text generation using deep learning: providing large-scale support for online learning communities

被引:23
作者
Du, Hanxiang [1 ]
Xing, Wanli [1 ]
Pei, Bo [1 ]
机构
[1] Univ Florida, Educ Technol, Sch Teaching & Learning, Gainesville, FL 32611 USA
基金
美国国家科学基金会;
关键词
Deep learning; artificial intelligence; online learning; language models; text generation; online communities; SOCIAL PRESENCE; COMMITMENT; SATISFACTION; BEHAVIOR; SENSE;
D O I
10.1080/10494820.2021.1993932
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
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.
引用
收藏
页码:5021 / 5036
页数:16
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