Catalyzing Python Learning: Assessing an LLM-based Conversational Agent

Abstract

The rapid rise of digital learning platforms has ushered in an era of educational transformation. While these platforms offer the advantage of scalability, they often fall short in facilitating meaningful interaction, which is pivotal for effective learning. Addressing this concern, our study introduces PyGuru 2.0, an innovative online learning environment for Python programming that aligns with the ICAP framework with an advanced conversational agent. We further investigate the interactions between students and a chatbot, employing a qualitative approach to comprehensively explore the diverse ways in which students interact with the chatbot. The interaction categories encompass a wide spectrum, including code assistance, error resolution, and conceptual explanation. In future, we plan to further elaborate on this coding scheme and see its impact on students’ learning outcomes.