1 citations · 3 across the 7 of their papers we have counts for
8 papers
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…
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…
Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modeling
Bryan Wong, Jong Woo Kim, Huazhu Fu +1
Vision-language models (VLMs) have recently been integrated into multiple instance learning (MIL) frameworks to address the challenge of few-shot, weakly supervised classification…
Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learning
Willmer Rafell Quinones Robles, Sakonporn Noree, Jongwoo Kim +3
Deep learning has shown strong potential in cancer classification from whole-slide images (WSIs), but the need for extensive expert annotations often limits its success. Annotation…
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…
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…
PreMix: Label-Efficient Multiple Instance Learning via Non-Contrastive Pre-training and Feature Mixing
Bryan Wong, Mun Yong Yi
Multiple instance learning (MIL) has emerged as a powerful framework for weakly supervised whole slide image (WSI) classification, enabling slide-level predictions without requirin…
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…