  The integration of Large Language Models (LLMs), especially ChatGPT, into
education is poised to revolutionize students' learning experiences by
introducing innovative conversational learning methodologies. To empower
students to fully leverage the capabilities of ChatGPT in educational
scenarios, understanding students' interaction patterns with ChatGPT is crucial
for instructors. However, this endeavor is challenging due to the absence of
datasets focused on student-ChatGPT conversations and the complexities in
identifying and analyzing the evolutional interaction patterns within
conversations. To address these challenges, we collected conversational data
from 48 students interacting with ChatGPT in a master's level data
visualization course over one semester. We then developed a coding scheme,
grounded in the literature on cognitive levels and thematic analysis, to
categorize students' interaction patterns with ChatGPT. Furthermore, we present
a visual analytics system, StuGPTViz, that tracks and compares temporal
patterns in student prompts and the quality of ChatGPT's responses at multiple
scales, revealing significant pedagogical insights for instructors. We
validated the system's effectiveness through expert interviews with six data
visualization instructors and three case studies. The results confirmed
StuGPTViz's capacity to enhance educators' insights into the pedagogical value
of ChatGPT. We also discussed the potential research opportunities of applying
visual analytics in education and developing AI-driven personalized learning
solutions.
