---
title: "BEA 2024 — all accepted papers with summaries and citations"
description: "All 58 papers of BEA 2024, the most cited first; those with our summary link to it. Among them: A World CLASSE Student Summary Corpus ↗."
canonical: "https://research.elamur.ai/free/en/venues/bea/2024/papers/"
---
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# BEA 2024: all accepted papers

58 papers · the most cited first · [← BEA 2024](https://md.research.elamur.ai/free/en/venues/bea/2024/)

1. **[A World CLASSE Student Summary Corpus ↗](https://aclanthology.org/2024.bea-1.9/)**Scott Crossley, Perpetual Baffour, Mihai Dascalu et al.
2. **[ANU at MLSP-2024: Prompt-based Lexical Simplification for English and Sinhala ↗](https://aclanthology.org/2024.bea-1.53/)**Sandaru Seneviratne, Hanna Suominen
3. **[Anna Karenina Strikes Again: Pre-Trained LLM Embeddings May Favor High-Performing Learners ↗](https://aclanthology.org/2024.bea-1.32/)**Abigail Gurin Schleifer, Beata Beigman Klebanov, Moriah Ariely et al.
4. **[Archaeology at MLSP 2024: Machine Translation for Lexical Complexity Prediction and Lexical Simplification ↗](https://aclanthology.org/2024.bea-1.55/)**Petru Cristea, Sergiu Nisioi
5. **[Assessing Student Explanations with Large Language Models Using Fine-Tuning and Few-Shot Learning ↗](https://aclanthology.org/2024.bea-1.33/)**Dan Carpenter, Wookhee Min, Seung Lee et al.
6. **[Automated Essay Scoring Using Grammatical Variety and Errors with Multi-Task Learning and Item Response Theory ↗](https://aclanthology.org/2024.bea-1.26/)**Kosuke Doi, Katsuhito Sudoh, Satoshi Nakamura
7. **[Automated Evaluation of Teacher Encouragement of Student-to-Student Interactions in a Simulated Classroom Discussion ↗](https://aclanthology.org/2024.bea-1.16/)**Michael Ilagan, Beata Beigman Klebanov, Jamie Mikeska
8. **[Automated Scoring of Clinical Patient Notes: Findings From the Kaggle Competition and Their Translation into Practice ↗](https://aclanthology.org/2024.bea-1.8/)**Victoria Yaneva, King Yiu Suen, Le An Ha et al.
9. **[Automated Sentence Generation for a Spaced Repetition Software ↗](https://aclanthology.org/2024.bea-1.29/)**Benjamin Paddags, Daniel Hershcovich, Valkyrie Savage
10. **[Automatic Crossword Clues Extraction for Language Learning ↗](https://aclanthology.org/2024.bea-1.31/)**Santiago Berruti, Arturo Collazo, Diego Sellanes et al.
11. **[BERT-IRT: Accelerating Item Piloting with BERT Embeddings and Explainable IRT Models ↗](https://aclanthology.org/2024.bea-1.35/)**Kevin P. Yancey, Andrew Runge, Geoffrey LaFlair et al.
12. **[Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts ↗](https://aclanthology.org/2024.bea-1.5/)**Donya Rooein, Paul Röttger, Anastassia Shaitarova et al.
13. **[Building Robust Content Scoring Models for Student Explanations of Social Justice Science Issues ↗](https://aclanthology.org/2024.bea-1.37/)**Allison Bradford, Kenneth Steimel, Brian Riordan et al.
14. **[Can GPT-4 do L2 analytic assessment? ↗](https://aclanthology.org/2024.bea-1.14/)**Stefano Bannò, Hari K. Vydana, Kate M. Knill et al.
15. **[Can Language Models Guess Your Identity? Analyzing Demographic Biases in AI Essay Scoring ↗](https://aclanthology.org/2024.bea-1.7/)**Alexander Kwako, Christopher Ormerod
16. **[Error Tracing in Programming: A Path to Personalised Feedback ↗](https://aclanthology.org/2024.bea-1.27/)**Martha Shaka, Diego Carraro, Kenneth Brown
17. **[Evaluating Vocabulary Usage in LLMs ↗](https://aclanthology.org/2024.bea-1.22/)**Matthew Durward, Christopher Thomson
18. **[Explainable AI in Language Learning: Linking Empirical Evidence and Theoretical Concepts in Proficiency and Readability Modeling of Portuguese ↗](https://aclanthology.org/2024.bea-1.17/)**Luisa Ribeiro-Flucht, Xiaobin Chen, Detmar Meurers
19. **[Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation ↗](https://aclanthology.org/2024.bea-1.23/)**Maja Stahl, Leon Biermann, Andreas Nehring et al.
20. **[Fairness in Automated Essay Scoring: A Comparative Analysis of Algorithms on German Learner Essays from Secondary Education ↗](https://aclanthology.org/2024.bea-1.18/)**Nils-Jonathan Schaller, Yuning Ding, Andrea Horbach et al.
21. **[Findings from the First Shared Task on Automated Prediction of Difficulty and Response Time for Multiple-Choice Questions ↗](https://aclanthology.org/2024.bea-1.39/)**Victoria Yaneva, Kai North, Peter Baldwin et al.
22. **[From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential ↗](https://aclanthology.org/2024.bea-1.38/)**Beata Beigman Klebanov, Michael Suhan, Tenaha O'Reilly et al.
23. **[GMU at MLSP 2024: Multilingual Lexical Simplification with Transformer Models ↗](https://aclanthology.org/2024.bea-1.57/)**Dhiman Goswami, Kai North, Marcos Zampieri
24. **[Harnessing GPT to Study Second Language Learner Essays: Can We Use Perplexity to Determine Linguistic Competence? ↗](https://aclanthology.org/2024.bea-1.34/)**Ricardo Muñoz Sánchez, Simon Dobnik, Elena Volodina
25. **[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? ↗](https://aclanthology.org/2024.bea-1.1/)**Nicy Scaria, Suma Dharani Chenna, Deepak Subramani
26. **[ISEP_Presidency_University at MLSP 2024 Shared Task: Using GPT-3.5 to Generate Substitutes for Lexical Simplification ↗](https://aclanthology.org/2024.bea-1.54/)**Benjamin Dutilleul, Mathis Debaillon, Sandeep Mathias
27. **[ITEC at BEA 2024 Shared Task: Predicting Difficulty and Response Time of Medical Exam Questions with Statistical, Machine Learning, and Language Models ↗](https://aclanthology.org/2024.bea-1.43/)**Anaïs Tack, Siem Buseyne, Changsheng Chen et al.
28. **[ITEC at MLSP 2024: Transferring Predictions of Lexical Difficulty from Non-Native Readers ↗](https://aclanthology.org/2024.bea-1.58/)**Anaïs Tack
29. **[Identifying Fairness Issues in Automatically Generated Testing Content ↗](https://aclanthology.org/2024.bea-1.20/)**Kevin Stowe, Benny Longwill, Alyssa Francis et al.
30. **[Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank ↗](https://aclanthology.org/2024.bea-1.19/)**Alexander Scarlatos, Wanyong Feng, Digory Smith et al.
31. **[Improving Readability Assessment with Ordinal Log-Loss ↗](https://aclanthology.org/2024.bea-1.28/)**Ho Hung Lim, John Lee
32. **[Improving Socratic Question Generation using Data Augmentation and Preference Optimization ↗](https://aclanthology.org/2024.bea-1.10/)**Nischal Ashok Kumar, Andrew Lan
33. **[Improving Transfer Learning for Early Forecasting of Academic Performance by Contextualizing Language Models ↗](https://aclanthology.org/2024.bea-1.13/)**Ahatsham Hayat, Bilal Khan, Mohammad Hasan
34. **[Item Difficulty and Response Time Prediction with Large Language Models: An Empirical Analysis of USMLE Items ↗](https://aclanthology.org/2024.bea-1.44/)**Okan Bulut, Guher Gorgun, Bin Tan
35. **[LLMs in Short Answer Scoring: Limitations and Promise of Zero-Shot and Few-Shot Approaches ↗](https://aclanthology.org/2024.bea-1.25/)**Imran Chamieh, Torsten Zesch, Klaus Giebermann
36. **[Large Language Model-based Pipeline for Item Difficulty and Response Time Estimation for Educational Assessments ↗](https://aclanthology.org/2024.bea-1.49/)**Hariram Veeramani, Surendrabikram Thapa, Natarajan Balaji Shankar et al.
37. **[Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction ↗](https://aclanthology.org/2024.bea-1.6/)**Masamune Kobayashi, Masato Mita, Mamoru Komachi
38. **[Leveraging Physical and Semantic Features of text item for Difficulty and Response Time Prediction of USMLE Questions ↗](https://aclanthology.org/2024.bea-1.46/)**Gummuluri Venkata Ravi Ram, Ashinee Kesanam, Anand Kumar M
39. **[Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models ↗](https://aclanthology.org/2024.bea-1.3/)**Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi et al.
40. **[Predicting Initial Essay Quality Scores to Increase the Efficiency of Comparative Judgment Assessments ↗](https://aclanthology.org/2024.bea-1.12/)**Michiel De Vrindt, Anaïs Tack, Renske Bouwer et al.
41. **[Predicting Item Difficulty and Item Response Time with Scalar-mixed Transformer Encoder Models and Rational Network Regression Heads ↗](https://aclanthology.org/2024.bea-1.40/)**Sebastian Gombert, Lukas Menzel, Daniele Di Mitri et al.
42. **[RETUYT-INCO at MLSP 2024: Experiments on Language Simplification using Embeddings, Classifiers and Large Language Models ↗](https://aclanthology.org/2024.bea-1.56/)**Ignacio Sastre, Leandro Alfonso, Facundo Fleitas et al.
43. **[Scoring with Confidence? – Exploring High-confidence Scoring for Saving Manual Grading Effort ↗](https://aclanthology.org/2024.bea-1.11/)**Marie Bexte, Andrea Horbach, Lena Schützler et al.
44. **[Synthetic Data Generation for Low-resource Grammatical Error Correction with Tagged Corruption Models ↗](https://aclanthology.org/2024.bea-1.2/)**Felix Stahlberg, Shankar Kumar
45. **[TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification? ↗](https://aclanthology.org/2024.bea-1.52/)**Taisei Enomoto, Hwichan Kim, Tosho Hirasawa et al.
46. **[The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline ↗](https://aclanthology.org/2024.bea-1.51/)**Matthew Shardlow, Fernando Alva-Manchego, Riza Batista-Navarro et al.
47. **[The British Council submission to the BEA 2024 shared task ↗](https://aclanthology.org/2024.bea-1.42/)**Mariano Felice, Zeynep Duran Karaoz
48. **[Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond ↗](https://aclanthology.org/2024.bea-1.21/)**Masato Mita, Keisuke Sakaguchi, Masato Hagiwara et al.
49. **[Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation](https://md.research.elamur.ai/free/en/papers/glandorf-2024-fine-grained-pedagogical-control-over/)**Dominik Glandorf, Detmar Meurers*our summary*
50. **[Transfer Learning of Argument Mining in Student Essays ↗](https://aclanthology.org/2024.bea-1.36/)**Yuning Ding, Julian Lohmann, Nils-Jonathan Schaller et al.
51. **[UNED team at BEA 2024 Shared Task: Testing different Input Formats for predicting Item Difficulty and Response Time in Medical Exams ↗](https://aclanthology.org/2024.bea-1.50/)**Alvaro Rodrigo, Sergio Moreno-Álvarez, Anselmo Peñas
52. **[UPN-ICC at BEA 2024 Shared Task: Leveraging LLMs for Multiple-Choice Questions Difficulty Prediction ↗](https://aclanthology.org/2024.bea-1.47/)**George Duenas, Sergio Jimenez, Geral Mateus Ferro
53. **[UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions ↗](https://aclanthology.org/2024.bea-1.41/)**Ana-Cristina Rogoz, Radu Tudor Ionescu
54. **[Using Adaptive Empathetic Responses for Teaching English ↗](https://aclanthology.org/2024.bea-1.4/)**Li Siyan, Teresa Shao, Julia Hirschberg et al.
55. **[Using Large Language Models to Assess Young Students' Writing Revisions ↗](https://aclanthology.org/2024.bea-1.30/)**Tianwen Li, Zhexiong Liu, Lindsay Matsumura et al.
56. **[Using Machine Learning to Predict Item Difficulty and Response Time in Medical Tests ↗](https://aclanthology.org/2024.bea-1.48/)**Mehrdad Yousefpoori-Naeim, Shayan Zargari, Zahra Hatami
57. **[Using Program Repair as a Proxy for Language Models' Feedback Ability in Programming Education ↗](https://aclanthology.org/2024.bea-1.15/)**Charles Koutcheme, Nicola Dainese, Arto Hellas
58. **[Utilizing Machine Learning to Predict Question Difficulty and Response Time for Enhanced Test Construction ↗](https://aclanthology.org/2024.bea-1.45/)**Rishikesh Fulari, Jonathan Rusert
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