@inproceedings{veeramani-etal-2024-large,
title = "Large Language Model-based Pipeline for Item Difficulty and Response Time Estimation for Educational Assessments",
author = "Veeramani, Hariram and
Thapa, Surendrabikram and
Shankar, Natarajan Balaji and
Alwan, Abeer",
editor = {Kochmar, Ekaterina and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bea-1.49/",
pages = "561--566",
abstract = "This work presents a novel framework for the automated prediction of item difficulty and response time within educational assessments. Utilizing data from the BEA 2024 Shared Task, we integrate Named Entity Recognition, Semantic Role Labeling, and linguistic features to prompt a Large Language Model (LLM). Our best approach achieves an RMSE of 0.308 for item difficulty and 27.474 for response time prediction, improving on the provided baseline. The framework`s adaptability is demonstrated on audio recordings of 3rd-8th graders from the Atlanta, Georgia area responding to the Test of Narrative Language. These results highlight the framework`s potential to enhance test development efficiency."
}
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%0 Conference Proceedings
%T Large Language Model-based Pipeline for Item Difficulty and Response Time Estimation for Educational Assessments
%A Veeramani, Hariram
%A Thapa, Surendrabikram
%A Shankar, Natarajan Balaji
%A Alwan, Abeer
%Y Kochmar, Ekaterina
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F veeramani-etal-2024-large
%X This work presents a novel framework for the automated prediction of item difficulty and response time within educational assessments. Utilizing data from the BEA 2024 Shared Task, we integrate Named Entity Recognition, Semantic Role Labeling, and linguistic features to prompt a Large Language Model (LLM). Our best approach achieves an RMSE of 0.308 for item difficulty and 27.474 for response time prediction, improving on the provided baseline. The framework‘s adaptability is demonstrated on audio recordings of 3rd-8th graders from the Atlanta, Georgia area responding to the Test of Narrative Language. These results highlight the framework‘s potential to enhance test development efficiency.
%U https://aclanthology.org/2024.bea-1.49/
%P 561-566
Markdown (Informal)
[Large Language Model-based Pipeline for Item Difficulty and Response Time Estimation for Educational Assessments](https://aclanthology.org/2024.bea-1.49/) (Veeramani et al., BEA 2024)
ACL