8 citations · 13 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Using State-of-the-Art Speech Models to Evaluate Oral Reading Fluency in Ghana
Owen Henkel, Hannah Horne-Robinson, Libby Hills +2
This paper reports on a set of three recent experiments utilizing large-scale speech models to evaluate the oral reading fluency (ORF) of students in Ghana. While ORF is a well-est…
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…
Leveraging Human Feedback to Scale Educational Datasets: Combining Crowdworkers and Comparative Judgement
Owen Henkel, Libby Hills
Machine Learning models have many potentially beneficial applications in education settings, but a key barrier to their development is securing enough data to train these models. L…