3 papers
cs.CV2025
Seeing the Big Picture: Evaluating Multimodal LLMs' Ability to Interpret and Grade Handwritten Student Work
Owen Henkel, Bill Roberts, Doug Jaffe +1
Recent advances in multimodal large language models (MLLMs) raise the question of their potential for grading, analyzing, and offering feedback on handwritten student classwork. Th…
cs.CL2024
Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education
Owen Henkel, Adam Boxer, Libby Hills +1
This paper presents reports on a series of experiments with a novel dataset evaluating how well Large Language Models (LLMs) can mark (i.e. grade) open text responses to short answ…
cs.CL2024
Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset
Owen Henkel, Libby Hills, Bill Roberts +1
Open-ended questions, which require students to produce multi-word, nontrivial responses, are a popular tool for formative assessment as they provide more specific insights into wh…