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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. 

Novice programming learners encounter programming errors on a regular basis. Resolving programming errors, which is also known as debugging, is not easy yet important to programming learning. Students with poor debugging ability hardly perform well on programming courses. A debugging learning trajectory which identifies learning goals, learning pathways, and instructional activities will benefit debugging learning activities development. This study aims to develop a debugging learning trajectory for text-based programming learners. This is accomplished through (1) analyzing programming errors in a logic programming learning environment and (2) examining existing literature on debugging analysis.