{"id":2584,"date":"2026-03-25T10:41:17","date_gmt":"2026-03-25T08:41:17","guid":{"rendered":"https:\/\/sites.uef.fi\/learning-analytics\/?page_id=2584"},"modified":"2026-03-25T12:18:31","modified_gmt":"2026-03-25T10:18:31","slug":"tna-festival-workshop-2026","status":"publish","type":"page","link":"https:\/\/sites.uef.fi\/learning-analytics\/tna-festival-workshop-2026\/","title":{"rendered":"Workshop: “Modeling Dynamics of Learning and Learners with the Transition Network Analysis Toolkit”"},"content":{"rendered":"\n

at Festival of Learning (Seoul, South Korea)<\/strong><\/h2>\n\n\n\n

27-28 June 2026<\/h3>\n\n\n\n

This workshop introduces participants to Transition Network Analysis (TNA), a comprehensive framework for modeling learning processes through rigorous statistical methods. Participants will explore how TNA enables rigorous analysis of learning dynamics\u2014with or without coding expertise. TNA includes a rich repertoire of tools and techniques for examining interaction data, including methods for visualizing and identifying recurring structural patterns such as dyads, triads, communities, and clusters. A key focus is TNA’s integration of statistical validation techniques including bootstrapping, permutation testing, and case-dropping, allowing researchers to validate individual network edges with p-values and effect sizes, compare patterns across learner subgroups, and explain observed dynamics with edge-level significance. The workshop covers TNA’s theoretical foundations and key variants: Frequency-based TNA, Attention Network Analysis, Heterogeneous TNA, and Co-occurrence Network Analysis. Through guided instruction and hands-on practice, attendees will learn to identify suitable data and research questions, perform data preprocessing, and apply TNA using both the tna R package and no-code platforms (tna-web and JTNA).<\/p>\n\n\n

\n \n
\n \"Kamila\"\n <\/div>\n

Kamila Misiejuk<\/p>\n

\n <\/svg> <\/div>\n <\/a>\n \n \n
\n \"Sonsoles\"\n <\/div>\n

Sonsoles L\u00f3pez-Pernas<\/p>\n

\n <\/svg> <\/div>\n <\/a>\n \n \n
\n \"Eduardo\n <\/div>\n

Eduardo Oliveira<\/p>\n

\n <\/svg> <\/div>\n <\/a>\n \n \n
\n \"Mohammed\"\n <\/div>\n

Mohammed Saqr<\/p>\n

\n <\/svg> <\/div>\n <\/a>\n \n <\/div>\n\n\n\n

What is TNA?<\/strong><\/h2>\n\n\n\n

TNA is a novel framework for modeling the learning process through a rich toolkit of rigorous, statistically validated methods. It provides an accessible yet powerful environment for researchers to perform sophisticated analyses\u2014without requiring coding expertise. TNA supports the computation of metrics at the graph, node, and edge levels, and facilitates the identification of recurring structures such as dyads, triads, communities, and clusters. TNA integrates statistical validation techniques \u2014including bootstrapping, permutation testing, and case-dropping\u2014 to assess the robustness and replicability of findings. This allows researchers to statistically validate each edge in the network with both p-values and effect sizes \u2014an unprecedented capability in process modeling. Furthermore, TNA enables comparison across subgroups and the explanation of observed patterns with edge-level statistical significance. By combining network analysis with statistical rigor, TNA supports theory development and hypothesis testing grounded in empirical evidence.<\/p>\n\n\n\n

The workshop\u2019<\/strong>s learning objectives<\/strong> include helping participants develop a solid understanding of theoretical foundations and methodological affordances of TNA and its key variants: Frequency-based TNA, Attention Network Analysis, and Co-occurrence TNA. Attendees will learn how to choose appropriate data and research questions for TNA, perform necessary data preprocessing, and apply the method using both point-and-click platforms where no code or programming experience is required (tna-web<\/a> and JTNA<\/a>) as well as code-based tools (tna R package<\/a>).<\/p>\n\n\n\n

Schedule<\/strong><\/h2>\n\n\n\n

9:00 – 9:15 Welcome & Workshop overview
9:15 – 10:00 Introduction to theoretical foundation and use cases in TNA
10:00 – 10:15\u00a0Coffee break<\/em>
10:15 – 10:45 Capturing human-AI dynamics with TNA
10:45 – 12:00 Paper presentations with plenum discussions
12:00 – 13:00 Lunch break<\/em>
13:00 – 14:00 Interactive demo of TNA Jamovi and TNA R package
14:00 – 14:45 Hands-on TNA and participatory exercises in small groups (I)
14:45 – 15:00 Coffee break<\/em>
15:00 – 16:00 Hands-on TNA and participatory exercises in small groups (II)
16:00 – 17:00 Closing, reflections, discussions
<\/p>\n\n\n\n

Call for papers<\/h3>\n\n\n\n

The presented papers will be published in a volume in Springer Communications in Computer and Information Science (CCIS). We accept empirical, methodological, theoretical, or software\/data contributions for this section of the workshop related to the following topics:<\/p>\n\n\n\n