NERIF: GPT-4V for Automatic Scoring of Drawn Models

Understand

Scoring student-drawn models is time-consuming.

• Recently released GPT-4V provides a unique opportunity to advance scientific modeling practices by leveraging the powerful image processing capability.
• To test this ability specifically for automatic scoring, we developed a method NERIF (Notation-Enhanced Rubric Instruction for Few-shot Learning) employing instructional note and rubrics to prompt GPT-4V to score students' drawn models for science phenomena.
• We randomly selected a set of balanced data (N = 900) that includes student-drawn models for six modeling assessment tasks.