| Popis: |
Informal learning is a significant part of lifelong learning. The rise of online communities as a new venue for informal learning has led to an increase in the availability of discourse data. As the dataset grows, it is feasible for scholars to understand the learning dynamics of these communities. However, the manual coding and analysis of such large datasets can be cost-prohibitive. Natural Language Processing (NLP) has been demonstrated to be a viable solution for analyzing large datasets in educational contexts. In this paper, we explore the application of NLP topic modeling method, Latent Dirichlet allocation (LDA), in understanding informal learning dynamic within an AI painting community. We collected data in two months from November 7, 2022, to January 8, 2023, and our findings show that major topics discussed in the space are around ethics, models, and procedures of AI painting, and topics updated over two months. |
