{"version":"1.0","provider_name":"Learning Analytics Unit","provider_url":"https:\/\/sites.uef.fi\/learning-analytics","title":"Transition Network Analysis Workshop - Learning Analytics Unit","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"qjHlmJDMpc\"><a href=\"https:\/\/sites.uef.fi\/learning-analytics\/tna-lak-workshop-2026\/\">Transition Network Analysis Workshop<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/sites.uef.fi\/learning-analytics\/tna-lak-workshop-2026\/embed\/#?secret=qjHlmJDMpc\" width=\"600\" height=\"338\" title=\"&#8220;Transition Network Analysis Workshop&#8221; &#8212; Learning Analytics Unit\" data-secret=\"qjHlmJDMpc\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/sites.uef.fi\/learning-analytics\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","description":"Our workshop at LAK26 introduces Transition Network Analysis (TNA), a flexible and powerful method for modeling learning processes through sequences of events. As such, TNA is well-suited for capturing the complex, dynamic, and probabilistic nature of human\u2013AI interactions. The workshop\u2019s learning objectives 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 identify appropriate data and research questions for TNA, perform necessary data preprocessing, and apply the method using both code-based tools (tna R package) and no-code platforms (tna-web and JTNA). The program combines theoretical foundations with practical experience, including lectures, hands-on exercises, group activities, and paper presentations. An open call for papers will invite contributions, with selected submissions published in Springer. A post-workshop editorial will summarize key insights and discussions. All materials\u2014including slides, tutorials, and software guides\u2014will be made available through the workshop website, which will serve as a long-term resource hub for participants and the broader community.","thumbnail_url":"https:\/\/sites.uef.fi\/learning-analytics\/wp-content\/uploads\/sites\/444\/2025\/10\/Screenshot-2025-10-07-at-19.17.39-1024x541.png"}