26 citations · 106 across the 11 of their papers we have counts for
4 papers · 1 filter
Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring
Xuansheng Wu, Padmaja Pravin Saraf, Gyeonggeon Lee +3
Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans a…
G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German
Ehsan Latif, Gyeong-Geon Lee, Knut Neumann +2
The advancement of natural language processing has paved the way for automated scoring systems in various languages, such as German (e.g., German BERT [G-BERT]). Automatically scor…
Applying Large Language Models and Chain-of-Thought for Automatic Scoring
Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu +2
This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written resp…
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring
Luyang Fang, Gyeong-Geon Lee, Xiaoming Zhai
Machine learning-based automatic scoring faces challenges with unbalanced student responses across scoring categories. To address this, we introduce a novel text data augmentation…