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

arXiv: https://arxiv.org/abs/2411.15486 · HTML: https://mlsanity.com/paper/2411.15486/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [A Review of Dynamic Network Models with Latent Variables](https://mlsanity.com/paper/1711.10421/) (2017)
- [The Gaussian Graphical Model in Cross-sectional and Time-series Data](https://mlsanity.com/paper/1609.04156/) (2016)
- [A comparative analysis of knowledge acquisition performance in complex networks](https://mlsanity.com/paper/2007.12028/) (2020)

## Built on by (most cited first, 3 of 3)

- [Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming](https://mlsanity.com/paper/2604.27134/) (2026)
- [Penny: Transition Network Analysis of Learner-Chatbot Interactions in Scaffolded EFL Writing](https://mlsanity.com/paper/2607.14575/) (2026)
- [Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?](https://mlsanity.com/paper/2508.10919/) (2025)

## Abstract (arXiv)

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.