Transition Network Analysis

Transition Network Analysis (TNA) is a novel learning analytics method that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and analyze patterns in learning process data. The method captures both relational and temporal aspects, offering a comprehensive and rigorous view of learning behaviors. The main features of TNA are:

  • Centrality analysis to identify important learning events.
  • Community detection to uncover behaviors that commonly occur together.
  • Clustering to reveal different temporal patterns in learning processes.
  • Significance testing to validate key transitions and remove noise.

TNA will be presented for the first time at the Learning Analytics & Knowledge conference (LAK) 2025.

  • Mohammed Saqr, Sonsoles López-Pernas, Tiina Törmänen, Rogers Kaliisa, Kamila Misiejuk, and Santtu Tikka. 2025. Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (LAK ’25). Association for Computing Machinery, New York, NY, USA, 351–361. https://doi.org/10.1145/3706468.3706513

TNA can be implemented using the R package of the same name, available on CRAN

  • Sonsoles López-Pernas, Mohammed Saqr, Santu Tikka (2024). tna: An R package for Transition Network Analysis. R package version 0.4.0, https://github.com/sonsoleslp/tna.

The reference manual is available in https://sonsoles.me/tna

There are three tutorials available about TNA:

  • Basic tutorial: Mohammed Saqr, Sonsoles López-Pernas, Santtu Tikka (2025). Mapping Relational Dynamics with Transition Network Analysis: A Primer and Tutorial. In M. Saqr & S. López-Pernas (Eds.), Advanced Learning Analytics Methods: AI, Precision and Complexity (in – press). Springer. https://lamethods.org/book2/chapters/ch15-tna/ch15-tna.html
  • Frequency-based TNA: Mohammed Saqr, Sonsoles López-Pernas, Santtu Tikka (2025). Capturing The Breadth and Dynamics of the Temporal Processes with Frequency Transition Network Analysis: A Primer and Tutorial. In M. Saqr & S. López-Pernas (Eds.), Advanced Learning Analytics Methods: AI, Precision and Complexity (in – press). Springer. https://lamethods.org/book2/chapters/ch16-ftna/ch16-ftna.html
  • Clustering: Sonsoles López-Pernas, Santtu Tikka, Mohammed Saqr (2025). Mining Patterns and Clusters with Transition Network Analysis: A Heterogeneity Approach. In M. Saqr & S. López-Pernas (Eds.), Advanced Learning Analytics Methods: AI, Precision and Complexity (in – press). Springer. https://lamethods.org/book2/chapters/ch17-tna-clusters/ch17-tna-clusters.html

We are also working on a Shiny web application to be able to use tna without coding: https://sonsoleslp.shinyapps.io/tna-app

Stay tuned to find out all new developments!