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StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions
This study introduces StuGPTViz, a visual analytics system to understand how students use ChatGPT in education. It helps educators gain insights into conversational learning patterns and AI
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Human-Computer Interaction
Background:
- Large Language Models (LLMs) like ChatGPT offer new educational possibilities.
- Understanding student interactions with AI tools is vital for effective pedagogy.
- Existing datasets and analysis methods for student-AI conversations are limited.
Purpose of the Study:
- To address the lack of data and analysis methods for student-ChatGPT interactions.
- To develop a system for tracking and analyzing student engagement with ChatGPT.
- To provide pedagogical insights for instructors utilizing AI in the classroom.
Main Methods:
- Collected conversational data from 48 students using ChatGPT in a data visualization course.
- Developed a coding scheme based on cognitive levels and thematic analysis.
- Created StuGPTViz, a visual analytics system to track temporal interaction patterns and response quality.
Main Results:
- StuGPTViz effectively tracks and compares student prompt patterns and ChatGPT response quality.
- The system provides significant pedagogical insights for instructors.
- Expert interviews and case studies validated the system's effectiveness in enhancing understanding of AI's pedagogical value.
Conclusions:
- Visual analytics systems like StuGPTViz can significantly enhance educators' insights into AI tools.
- There are substantial research opportunities in applying visual analytics to education.
- This work paves the way for AI-driven personalized learning solutions.
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