2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Consistently Good vs. Occasionally Great: A Rubric for Open-Ended Feedback Quality from Humans and Machines
Binglin Chen, Rajarshi Haldar, Max Fowler +2
Providing high-quality feedback on student work is essential for learning, yet delivering such feedback at scale remains challenging. In this paper, we focus on feedback for open-e…
Automated Grading of Handwritten Mathematics Using Vision-Capable LLMs
Jacob Levine, Miguel Aenlle, Craig Zilles +2
Automated grading systems have enabled scalable assessment for many response types, but handwritten mathematics remains a barrier due to the complexity of multi-step solutions. Vis…
Counting the Trees in the Forest: Evaluating Prompt Segmentation for Classifying Code Comprehension Level
David H. Smith, Max Fowler, Paul Denny +1
Reading and understanding code are fundamental skills for novice programmers, and especially important with the growing prevalence of AI-generated code and the need to evaluate its…
ReDefining Code Comprehension: Function Naming as a Mechanism for Evaluating Code Comprehension
David H. Smith, Max Fowler, Paul Denny +1
"Explain in Plain English" (EiPE) questions are widely used to assess code comprehension skills but are challenging to grade automatically. Recent approaches like Code Generation B…
Code Generation Based Grading: Evaluating an Auto-grading Mechanism for "Explain-in-Plain-English" Questions
David H. Smith, Craig Zilles
Comprehending and elucidating the purpose of code is often cited as being a key learning objective within introductory programming courses. To address this objective ``Explain-in-P…