19 citations · 29 across the 26 of their papers we have counts for
13 papers · 1 filter
Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?
Unggi Lee, Haeun Park
Knowledge tracing (KT) models need many logged learners, so a new course or platform starts without a usable model. In LLM-based KT the LLM generates the answer, which we call Syst…
SpecialEduBench: Benchmarking Vision-Language Models on Knowledge, Skill, and Attitude in Language Intervention for Autistic Children
Jihoi Na, Taeyeong Kim, Sungjune Kong +11
Language is the target of most early intervention for autistic children. Because the goal and the method change from child to child, the work falls to a teacher who takes one child…
Not the Dimension, the Norm: What Matters in Gradient-Free Weight Perturbation of Language Models
Taeyeong Kim, Ahhyun Kim, TaeHyeon Kim +1
Adapting a language model to a task no longer requires training all of its weights, and a line of parameter-efficient methods has driven the trainable count from billions down to a…
LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring
Unggi Lee, Minchul Shin, Yeil Jeong +5
Aligning LLMs for math tutoring typically requires RL-based training with multi-GPU infrastructure. We investigate whether training-free prompt optimization-evolving only the syste…
Are Video Models Zero-Shot Learners and Reasoners in Education? EduVideoBench, A Knowledge-Skills-Attitude Benchmark for Educational Video Generation
Unggi Lee, Hoyoung Ahn, Yoon Choi +16
Video generation models (VGMs) are rapidly entering classrooms, yet existing benchmarks evaluate only perceptual quality, intrinsic faithfulness, generic safety, or video as a reas…
TeachObs: A Human-Validated Benchmark for Multimodal Teaching Observation and Model Evaluation
Yeil Jeong, Youngjin Yoo, Jiyoung Bae +5
Classroom videos contain observable teaching practices, but their pedagogical and visual signals are rarely organized in forms suitable for model evaluation. We present \textit{Tea…
Rewarding How Models Think Pedagogically: Integrating Pedagogical Reasoning and Thinking Rewards for LLMs in Education
Unggi Lee, Jiyeong Bae, Jaehyeon Park +7
Large language models (LLMs) are increasingly deployed as intelligent tutoring systems, yet research on optimizing LLMs specifically for educational contexts remains limited. Recen…
OpenLearnLM Benchmark: A Unified Framework for Evaluating Knowledge, Skill, and Attitude in Educational Large Language Models
Unggi Lee, Sookbun Lee, Heungsoo Choi +11
Large Language Models are increasingly deployed as educational tools, yet existing benchmarks focus on narrow skills and lack grounding in learning sciences. We introduce OpenLearn…
Pedagogical Alignment for Vision-Language-Action Models: A Comprehensive Framework for Data, Architecture, and Evaluation in Education
Unggi Lee, Jahyun Jeong, Sunyoung Shin +12
Science demonstrations are important for effective STEM education, yet teachers face challenges in conducting them safely and consistently across multiple occasions, where robotics…
A Training-Free Large Reasoning Model-based Knowledge Tracing Framework for Unified Prediction and Prescription
Unggi Lee, Joo Young Kim, Ran Ju +2
Knowledge Tracing (KT) aims to estimate a learner's evolving mastery based on interaction histories. Recent studies have explored Large Language Models (LLMs) for KT via autoregres…
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Unggi Lee, Jiyeong Bae, Yeonji Jung +9
Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introdu…
Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task
Unggi Lee, Jiyeong Bae, Dohee Kim +6
Knowledge Tracing (KT) is a critical task in online learning for modeling student knowledge over time. Despite the success of deep learning-based KT models, which rely on sequences…
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction
Unggi Lee, Sungjun Yoon, Joon Seo Yun +4
This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Desp…