Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes

TL;DR

Transition Network Analysis (TNA) introduces a theory-informed framework that unifies Stochastic Process Mining and probabilistic networks to model, visualize, and identify temporal learning patterns. By representing learning events and their transitions as a directed, weighted graph with a Markov-based transition matrix $\mathbf{P}$, TNA enables centrality analysis, pattern mining (dyads, cliques, communities), and covariate explanations, reinforced by bootstrap-based edge validity testing. The method is demonstrated on a case study with $n=191$ students in 24 groups, revealing central roles for understanding and task enactment, and showing distinct clusters of transition dynamics that relate to group size, course, and performance. The work contributes a robust, extensible toolkit for analyzing dynamic learning processes and offers a path toward longitudinal TNA and broader validity testing, with practical implications for diagnosing and guiding collaborative learning interventions.

Abstract

This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a single lens offers capabilities beyond either framework, including centralities to capture important learning events, community detection to identify behavior patterns, and clustering to reveal temporal patterns. Furthermore, TNA introduces several significance tests that go beyond either method and add rigor to the analysis. Here, we introduce the theoretical and mathematical foundations of TNA and we demonstrate the functionalities of TNA with a case study where students (n=191) engaged in small-group collaboration to map patterns of group dynamics using the theories of co-regulation and socially-shared regulated learning. The analysis revealed that TNA can map the regulatory processes as well as identify important events, patterns, and clusters. Bootstrap validation established the significant transitions and eliminated spurious transitions. As such, TNA can capture learning dynamics and provide a robust framework for investigating the temporal evolution of learning processes. Future directions include -- inter alia -- expanding estimation methods, reliability assessment, and building longitudinal TNA.

Figures (7)

• Figure 1: Outline of the TNA method: The transitions are captured from event logs with Markov models and represented as networks. TNA allows the discovery of different patterns, communities and clusters, as well as using covariates to explain these clusters. TNA models can also be tested for validity and stability using null models and bootstrapping.
• Figure 3: Left: A TNA plot of group collaborative interactions in a project; Right top: A bar chart representing the betweenness centrality of each code; Right bottom: A bar chart showing the in-strength centralities of each code.
• Figure 4: Dyads (top), triads (middle), and communities (bottom)
• Figure 5: Left: centrality measures of each cluster. Right: Transition network of each cluster
• Figure 6: Subtraction TNA network comparing high- and low-performing students that shows transition probabilities of high-performing students.