Capturing The Temporal Dynamics of Learner Interactions In Moocs: A Comprehensive Approach With Longitudinal And Inferential Network Analysis

While research on social network analysis is abundant and less frequently so temporal network analysis, research that uses inferential temporal network methods is barely existent. This paper aims to fill this gap by conducting a comparative analysis of temporal networks and inferential longitudinal network methods in the context of learner interactions in Massive Open Online Courses (MOOCs). We focus on three prominent methods: Temporal Network Analysis (TNA), Temporal Exponential Random Graph Models (TERGM) and Simulation Investigation for Empirical Network Analysis (SIENA). Using a five-week Nature Education MOOC as a case study, we compared the features, metrics of each method as well as their understanding of using network to analyze learner interactions. TNA focuses on describing and visualizing temporal changes in network structure, while TERGM and SIENA view networks as evolving systems influenced by individual behaviors and structural dependencies. TERGM treats network changes as a joint of random processes, while SIENA emphasizes the agency of learners and analyzes continuous network evolution. The findings provide guidelines for researchers and educators to select appropriate network analysis methods for temporal studies in educational contexts.