LAUSR

Instructional design and technology (IDT) professionals participate in communities of practice (CoPs) on Facebook to seek pedagogical and educational technology advice for solving instructional design (ID) problems. Much of the IDT literature has focused on formal educational environments and not on nonformal settings outside the classroom and beyond formal education. Further analysis of tacit or practical knowledge exchanged among community members is required to understand the purpose, functions, and organizational knowledge capital in online CoPs. To fill this gap, this study uses natural language processing (NLP) to analyze the practical knowledge of 6,066 anonymized users’ posts from four large public IDT CoPs on Facebook from September 2017 to September 2020 after cleaning the dataset. User posts were publicly available and required no password authentication for access, including Instructional Designer (4,717), Designers for Learning (228), Adobe Captivate Users (599), and Articulate Storyline (522). The proposed methodology aims to extract practical knowledge of individual online CoPs in three parts. First, the characteristics of written communication among members are extracted by calculating word and sentence lengths, word frequencies, and contiguous words. Second, the characteristics of members’ exchange of practical knowledge are obtained through sentiment identification, entity recognition, and relationships between pedagogical and educational technology entities. Third, the functions of individual online CoPs are developed through topic modeling with latent Dirichlet allocation (LDA) and BERTopic. The findings suggest similarities and differences among IDT CoPs, different resource distribution conventions, and members exchanging pedagogical and educational technology advice. The study highlights the need for pedagogical foundations to support instructional and technical decisions, mechanisms for self-assessment of practical knowledge concerning IDT competencies, community protocols for addressing misconceptions about learning, onboarding materials for new members, and new topic structures to classify practical knowledge. NLP tasks are implemented using Python libraries to support the future development of awareness tools.