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ORCID19th BEA 2024: Mexico City, Mexico
- Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan:
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2024, Mexico City, Mexico, June 20, 2024. Association for Computational Linguistics 2024, ISBN 979-8-89176-100-1 - Nicy Scaria
, Suma Dharani Chenna, Deepak N. Subramani:
How Good are Modern LLMs in Generating Relevant and High-Quality Questions at Different Bloom's Skill Levels for Indian High School Social Science Curriculum? 1-10 - Felix Stahlberg, Shankar Kumar:
Synthetic Data Generation for Low-resource Grammatical Error Correction with Tagged Corruption Models. 11-16 - Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi, Artem N. Chernodub, Oleksandr Korniienko, Igor Samokhin:
Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models. 17-33 - Siyan Li, Teresa Shao, Julia Hirschberg, Zhou Yu:
Using Adaptive Empathetic Responses for Teaching English. 34-53 - Donya Rooein, Paul Röttger, Anastassia Shaitarova, Dirk Hovy:
Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts. 54-67 - Masamune Kobayashi, Masato Mita, Mamoru Komachi:
Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction. 68-77 - Alexander Kwako, Christopher Michael Ormerod:
Can Language Models Guess Your Identity? Analyzing Demographic Biases in AI Essay Scoring. 78-86 - Victoria Yaneva, King Yiu Suen, Le An Ha, Janet Mee, Milton Quranda, Polina Harik:
Automated Scoring of Clinical Patient Notes: Findings From the Kaggle Competition and Their Translation into Practice. 87-98 - Scott A. Crossley, Perpetual Baffour, Mihai Dascalu, Stefan Ruseti:
A World CLASSE Student Summary Corpus. 99-107 - Nischal Ashok Kumar, Andrew S. Lan:
Improving Socratic Question Generation using Data Augmentation and Preference Optimization. 108-118 - Marie Bexte, Andrea Horbach, Lena Schützler, Oliver Christ, Torsten Zesch:
Scoring with Confidence? - Exploring High-confidence Scoring for Saving Manual Grading Effort. 119-124 - Michiel De Vrindt, Anaïs Tack, Renske Bouwer, Wim Van Den Noortgate, Marije Lesterhuis:
Predicting Initial Essay Quality Scores to Increase the Efficiency of Comparative Judgment Assessments. 125-136 - Ahatsham Hayat, Bilal Khan, Mohammad Hasan:
Improving Transfer Learning for Early Forecasting of Academic Performance by Contextualizing Language Models. 137-148 - Stefano Bannò, Hari Krishna Vydana, Kate M. Knill, Mark J. F. Gales:
Can GPT-4 do L2 analytic assessment? 149-164 - Charles Koutcheme, Nicola Dainese, Arto Hellas:
Using Program Repair as a Proxy for Language Models' Feedback Ability in Programming Education. 165-181 - Michael Ilagan, Beata Beigman Klebanov, Jamie N. Mikeska:
Automated Evaluation of Teacher Encouragement of Student-to-Student Interactions in a Simulated Classroom Discussion. 182-198 - Luisa Ribeiro-Flucht, Xiaobin Chen, Detmar Meurers:
Explainable AI in Language Learning: Linking Empirical Evidence and Theoretical Concepts in Proficiency and Readability Modeling of Portuguese. 199-209 - Nils-Jonathan Schaller, Yuning Ding, Andrea Horbach, Jennifer Meyer, Thorben Jansen:
Fairness in Automated Essay Scoring: A Comparative Analysis of Algorithms on German Learner Essays from Secondary Education. 210-221 - Alexander Scarlatos, Wanyong Feng, Andrew S. Lan, Simon Woodhead, Digory Smith:
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank. 222-231 - Kevin Stowe, Benny Longwill, Alyssa Francis, Tatsuya Aoyama, Debanjan Ghosh, Swapna Somasundaran:
Identifying Fairness Issues in Automatically Generated Testing Content. 232-250 - Masato Mita, Keisuke Sakaguchi, Masato Hagiwara, Tomoya Mizumoto, Jun Suzuki, Kentaro Inui:
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond. 251-265 - Matthew Durward, Christopher Thomson:
Evaluating Vocabulary Usage in LLMs. 266-282 - Maja Stahl, Leon Biermann, Andreas Nehring, Henning Wachsmuth:
Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation. 283-298 - Dominik Glandorf, Detmar Meurers:
Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation. 299-308 - Imran Chamieh, Torsten Zesch, Klaus Giebermann:
LLMs in Short Answer Scoring: Limitations and Promise of Zero-Shot and Few-Shot Approaches. 309-315 - Kosuke Doi, Katsuhito Sudoh, Satoshi Nakamura:
Automated Essay Scoring Using Grammatical Variety and Errors with Multi-Task Learning and Item Response Theory. 316-329 - Martha Shaka, Diego Carraro, Kenneth N. Brown:
Error Tracing in Programming: A Path to Personalised Feedback. 330-342 - Ho Hung Lim, John Lee:
Improving Readability Assessment with Ordinal Log-Loss. 343-350 - Benjamin Paddags, Daniel Hershcovich, Valkyrie Savage:
Automated Sentence Generation for a Spaced Repetition Software. 351-364 - Tianwen Li, Zhexiong Liu, Lindsay Clare Matsumura, Elaine Wang, Diane J. Litman, Richard Correnti:
Using Large Language Models to Assess Young Students' Writing Revisions. 365-380 - Santiago Berruti, Arturo Collazo, Diego Sellanes, Aiala Rosá, Luis Chiruzzo:
Automatic Crossword Clues Extraction for Language Learning. 381-390 - Abigail Gurin Schleifer, Beata Beigman Klebanov, Moriah Ariely, Giora Alexandron:
Anna Karenina Strikes Again: Pre-Trained LLM Embeddings May Favor High-Performing Learners. 391-402 - Dan Carpenter, Wookhee Min, Seung Y. Lee, Gamze Ozogul, Xiaoying Zheng, James C. Lester:
Assessing Student Explanations with Large Language Models Using Fine-Tuning and Few-Shot Learning. 403-413 - Ricardo Muñoz Sánchez, Simon Dobnik, Elena Volodina:
Harnessing GPT to Study Second Language Learner Essays: Can We Use Perplexity to Determine Linguistic Competence? 414-427 - Kevin P. Yancey, Andrew Runge, Geoffrey T. LaFlair, Phoebe Mulcaire:
BERT-IRT: Accelerating Item Piloting with BERT Embeddings and Explainable IRT Models. 428-438 - Yuning Ding, Julian F. Lohmann, Nils-Jonathan Schaller, Thorben Jansen, Andrea Horbach:
Transfer Learning of Argument Mining in Student Essays. 439-449 - Allison Bradford, Kenneth Steimel, Brian Riordan, Marcia C. Linn:
Building Robust Content Scoring Models for Student Explanations of Social Justice Science Issues. 450-458 - Beata Beigman Klebanov, Michael Suhan, Tenaha O'Reilly, Zuowei Wang:
From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential. 459-469 - Victoria Yaneva, Kai North, Peter Baldwin, Le An Ha, Saed Rezayi, Yiyun Zhou, Sagnik Ray Choudhury, Polina Harik, Brian Clauser:
Findings from the First Shared Task on Automated Prediction of Difficulty and Response Time for Multiple-Choice Questions. 470-482 - Sebastian Gombert, Lukas Menzel, Daniele Di Mitri, Hendrik Drachsler:
Predicting Item Difficulty and Item Response Time with Scalar-mixed Transformer Encoder Models and Rational Network Regression Heads. 483-492 - Ana-Cristina Rogoz, Radu Tudor Ionescu:
UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions. 493-502 - Mariano Felice, Zeynep Duran Karaoz:
The British Council submission to the BEA 2024 shared task. 503-511 - Anaïs Tack, Siem Buseyne, Changsheng Chen, Robbe D'hondt, Michiel De Vrindt, Alireza Gharahighehi, Sameh Metwaly, Felipe Kenji Nakano, Ann-Sophie Noreillie:
ITEC at BEA 2024 Shared Task: Predicting Difficulty and Response Time of Medical Exam Questions with Statistical, Machine Learning, and Language Models. 512-521 - Okan Bulut, Guher Gorgun, Bin Tan:
Item Difficulty and Response Time Prediction with Large Language Models: An Empirical Analysis of USMLE Items. 522-527 - Rishikesh Fulari, Jonathan Rusert:
Utilizing Machine Learning to Predict Question Difficulty and Response Time for Enhanced Test Construction. 528-533 - Gummuluri Venkata Ravi Ram, Ashinee Kesanam, Anand Kumar M:
Leveraging Physical and Semantic Features of text item for Difficulty and Response Time Prediction of USMLE Questions. 534-541 - George Dueñas, Sergio Jimenez, Geral Mateus Ferro:
UPN-ICC at BEA 2024 Shared Task: Leveraging LLMs for Multiple-Choice Questions Difficulty Prediction. 542-550 - Mehrdad Yousefpoori-Naeim, Shayan Zargari, Zahra Hatami:
Using Machine Learning to Predict Item Difficulty and Response Time in Medical Tests. 551-560 - Hariram Veeramani, Surendrabikram Thapa, Natarajan Balaji Shankar, Abeer Alwan:
Large Language Model-based Pipeline for Item Difficulty and Response Time Estimation for Educational Assessments. 561-566 - Álvaro Rodrigo, Sergio Moreno-Álvarez, Anselmo Peñas:
UNED team at BEA 2024 Shared Task: Testing different Input Formats for predicting Item Difficulty and Response Time in Medical Exams. 567-570 - Matthew Shardlow, Fernando Alva-Manchego, Riza Batista-Navarro, Stefan Bott, Saúl Calderón Ramírez, Rémi Cardon, Thomas François, Akio Hayakawa, Andrea Horbach, Anna Hülsing, Yusuke Ide, Joseph Marvin Imperial, Adam Nohejl, Kai North, Laura Occhipinti, Nelson Perez-Rojas, Nishat Raihan, Tharindu Ranasinghe, Martin Solis-Salazar, Sanja Stajner, Marcos Zampieri, Horacio Saggion:
The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline. 571-589 - Taisei Enomoto, Hwichan Kim, Tosho Hirasawa, Yoshinari Nagai, Ayako Sato, Kyotaro Nakajima, Mamoru Komachi:
TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification? 590-598 - Sandaru Seneviratne, Hanna Suominen:
ANU at MLSP-2024: Prompt-based Lexical Simplification for English and Sinhala. 599-604 - Benjamin Dutilleul, Mathis Debaillon, Sandeep Mathias:
ISEP_Presidency_University at MLSP 2024 Shared Task: Using GPT-3.5 to Generate Substitutes for Lexical Simplification. 605-609 - Petru Cristea, Sergiu Nisioi:
Archaeology at MLSP 2024: Machine Translation for Lexical Complexity Prediction and Lexical Simplification. 610-617 - Ignacio Sastre, Leandro Alfonso, Facundo Fleitas, Federico Gil, Andrés Lucas, Tomás Spoturno, Santiago Góngora, Aiala Rosá, Luis Chiruzzo:
RETUYT-INCO at MLSP 2024: Experiments on Language Simplification using Embeddings, Classifiers and Large Language Models. 618-626 - Dhiman Goswami, Kai North, Marcos Zampieri:
GMU at MLSP 2024: Multilingual Lexical Simplification with Transformer Models. 627-634 - Anaïs Tack:
ITEC at MLSP 2024: Transferring Predictions of Lexical Difficulty from Non-Native Readers. 635-639
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