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VizQStudio: Iterative Visualization Literacy MCQs Design With Simulated Students
Summary
VizQStudio uses AI-simulated students to help instructors create better visualization literacy multiple-choice questions (MCQs). This iterative design process supports learning gains and offers flexibility in assessment creation.
Area of Science:
- Educational Technology
- Data Visualization
- Artificial Intelligence in Education
Background:
- Designing effective visualization literacy multiple-choice questions (MCQs) is complex, requiring integration of multimodal elements and catering to diverse learners.
- Current assessment methods often use fixed item banks, limiting adaptive and iterative design processes for educational content.
Purpose of the Study:
- To introduce VizQStudio, a visual analytics system aiding instructors in the iterative design and refinement of visualization literacy MCQs.
- To leverage MLLM-powered simulated students for exploring question design, identifying misconceptions, and calibrating difficulty before classroom deployment.
Main Methods:
- Developed VizQStudio, a visual analytics system integrating MLLM-based student simulations with customizable student profiles.
- Conducted a mixed-method evaluation including expert interviews, case studies, classroom deployment, and a large-scale online study.
Main Results:
- MCQs developed using VizQStudio demonstrated measurable learning gains in students.
- The system provided flexibility and scalability in MCQ design, with outcomes comparable to established benchmarks in an exploratory online sample.
Conclusions:
- MLLM-based student simulation can serve as a valuable design-time aid for assessment authoring.
- VizQStudio offers insights into instructor-centered, iterative, and responsible AI use for multimodal assessment design in visualization literacy and beyond.
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