Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Henkel, Owen"'
Autor:
Levonian, Zachary, Henkel, Owen
Large language models (LLMs) are flexible, personalizable, and available, which makes their use within Intelligent Tutoring Systems (ITSs) appealing. However, that flexibility creates risks: inaccuracies, harmful content, and non-curricular material.
Externí odkaz:
http://arxiv.org/abs/2407.04915
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 answer questions, Specifically, we explore how well different combinations
Externí odkaz:
http://arxiv.org/abs/2405.02985
This study evaluates the impact of Rori, an AI powered conversational math tutor accessible via WhatsApp, on the math performance of approximately 1,000 students in grades 3-9 across 11 schools in Ghana. Each school was assigned to a treatment group
Externí odkaz:
http://arxiv.org/abs/2402.09809
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 what students do and don't know. However, grading open-ended questions c
Externí odkaz:
http://arxiv.org/abs/2310.18373
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-established measure of foundational literacy, assessing it typically requ
Externí odkaz:
http://arxiv.org/abs/2310.17606
Autor:
Levonian, Zachary, Li, Chenglu, Zhu, Wangda, Gade, Anoushka, Henkel, Owen, Postle, Millie-Ellen, Xing, Wanli
For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions
Externí odkaz:
http://arxiv.org/abs/2310.03184
Autor:
Henkel, Owen, Hills, Libby
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. Labelling educational data has traditionally relied on highly skilled r
Externí odkaz:
http://arxiv.org/abs/2305.12894