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) is a new framework combining stochastic process mining and probabilistic graphs to model, visualize, and analyze temporal learning patterns, revealing key events and behaviors in student learning data.

Contribution

The paper introduces TNA, a novel method integrating relational and temporal analysis for learning processes, with new significance tests and validation techniques.

Findings

TNA successfully mapped group dynamics and identified key learning events.

Bootstrap validation confirmed the significance of detected transitions.

TNA revealed meaningful clusters and temporal patterns in student collaboration data.

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.