Investigating Instructor and Peer Instructional Behaviours in a Large Online Discussion Forum Using Temporal Dynamic Analytics
DOI:
https://doi.org/10.18608/jla.2026.9235Keywords:
instructional behaviours, online discussion forums, temporal dynamics, peer interaction, learning analytics, research paperAbstract
Online discussion forums are a key data source in STEM education for analyzing student interaction, knowledge construction, and social learning. From a learning analytics perspective, understanding the temporal dynamics of instructional behaviours, for example, the differences between instructors and peers, can offer valuable insights into how instructional support unfolds in online discussions. However, few studies have examined these interactions with attention to their sequential and temporal organization. This study adopts a learning analytics approach to investigate the behavioural dynamics of instructional support in a large-scale online math discussion forum. We analyzed 83,569 posts from Math Nation, a widely used online learning platform supporting K–12 math education in the United States. Instructional behaviours were automatically coded based on a theoretically grounded coding scheme using transformer-based language models, and their temporal patterns were modelled using multilevel vector autoregression. Network visualizations were employed to illustrate the dynamic relationships among instructional strategies. Our findings show that peers posted more frequently than instructors and were more likely to engage in acknowledgement and feedback behaviours. When examining behaviour proportions and temporal transitions within discussion threads, peer interactions were associated with more diverse behavioural sequences, whereas instructor interactions exhibited more focused and directive patterns. In mixed-participant threads, instructors often responded following peer contributions, which was associated with a reduced likelihood of subsequent direct peer interventions. Rather than making claims about instructional effectiveness or learning outcomes, this work contributes to the field of learning analytics by demonstrating how natural language processing (NLP)-based behaviour modelling can be integrated with temporal interaction analysis to characterize role-based instructional support dynamics in asynchronous, discussion-based STEM learning environments.
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