Leveraging Educational Socratic Chatbot Conversations for Extracting Learning Analytics Insights

The growing utilization of generative artificial intelligence (GenAI) has sparked discussions regarding integrating these tools into educational settings to enrich the learning experience for teachers and students. Self-regulated learning (SRL) research is pivotal in addressing this inquiry. One prevalent manifestation of GenAI is the large-language model chatbot, enabling users to seek information and assistance. This paper aims to showcase how student interaction data with a chatbot can be used in learning analytics to gain insights into self-regulated learning. This is achieved by adapting existing SRL frameworks to comprehend students' interaction with an educational Socratic chatbot for a statistics class at the introductory undergraduate level. Chatbot conversations from students are categorized into learning actions and processes using the framework's process-action library. Thereafter, we analyze this data through ordered epistemic network analysis, furnishing valuable insights into how different students interact with the chatbot.