6 papers
FeedEval: Pedagogically Aligned Evaluation of LLM-Generated Essay Feedback
Seongyeub Chu, Jongwoo Kim, Munyong Yi
Going beyond the prediction of numerical scores, recent research in automated essay scoring has increasingly emphasized the generation of high-quality feedback that provides justif…
Aspect-Aware MOOC Recommendation in a Heterogeneous Network
Seongyeub Chu, Jongwoo Kim, Mun Yong Yi
MOOC recommendation systems have received increasing attention to help learners navigate and select preferred learning content. Traditional methods such as collaborative filtering…
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning
Jongwoo Kim, Seongyeub Chu, Hyeongmin Park +3
Recent advancements in graph neural networks (GNNs) and heterogeneous GNNs (HGNNs) have advanced node embeddings and relationship learning for various tasks. However, existing meth…
Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing
JongWoo Kim, SeongYeub Chu, Bryan Wong +1
Large Language Models (LLMs) have recently emerged as promising tools for knowledge tracing (KT) due to their strong reasoning and generalization abilities. While recent LLM-based…
Think Together and Work Better: Combining Humans' and LLMs' Think-Aloud Outcomes for Effective Text Evaluation
SeongYeub Chu, JongWoo Kim, MunYong Yi
This study introduces \textbf{InteractEval}, a framework that integrates human expertise and Large Language Models (LLMs) using the Think-Aloud (TA) method to generate attributes f…
Rationale Behind Essay Scores: Enhancing S-LLM's Multi-Trait Essay Scoring with Rationale Generated by LLMs
SeongYeub Chu, JongWoo Kim, Bryan Wong +1
Existing automated essay scoring (AES) has solely relied on essay text without using explanatory rationales for the scores, thereby forgoing an opportunity to capture the specific…