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:

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 have released a plugin to work with TNA using the desktop app Jamovi: https://github.com/sonsoleslp/jTNA

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

  • López-Pernas, S., Tikka, S., Misiejuk, K., & Saqr, M. (2026). tna-shiny: Advanced Analytics Just a Few Clicks Away. Proceedings TEEM 2025: Thirteennth International Conference on Technological Ecosystems for Enhancing Multiculturality. TEEM 2025. Lecture Notes in Computer Science. TEEM 2025, Salamanca, Spain.

Stay tuned to find out all new developments!

Empirical papers using TNA

  • López-Pernas, S., Misiejuk, K., Kaliisa, R., & Saqr, M. (2025). Capturing the process of students’ AI interactions when creating and learning complex network structures. IEEE Transactions on Learning Technologies, 1–13. https://doi.org/10.1109/tlt.2025.3568599
  • Misiejuk, K., López-Pernas, S., Rogers, K., & Saqr, M. (2025). Learning together: Modeling the process of student-AI interactions when generating learning resources. Proceedings of 12th TEEM Conference. https://doi.org/10.1007/978-981-96-5658-5_45.
  • Törmänen, T., Saqr, M., López-Pernas, S., Mänty, K., Suoraniemi, J., Heikkala, N., & Järvenoja, H. (2025). Emotional dynamics and regulation in collaborative learning. Learning and Instruction, 100, 102188. https://doi.org/10.1016/j.learninstruc.2025.102188
  • Kilanioti, I., Saqr, M., & López-Pernas, S. (2026). Exploring the Process of Students’ Learning in the Laboratory with Transition Network Analysis. Proceedings TEEM 2025: Thirteennth International Conference on Technological Ecosystems for Enhancing Multiculturality. TEEM 2025. Lecture Notes in Computer Science. TEEM 2025, Salamanca, Spain.