5 citations · 13 across the 11 of their papers we have counts for
11 papers
Is Mathematical Problem-Solving Expertise in Large Language Models Associated with Assessment Performance?
Liang Zhang, Yu Fu, Xinyi Jin
Large Language Models (LLMs) are increasingly used in math education not only as problem solvers but also as assessors of learners' reasoning. However, it remains unclear whether s…
Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving
Liang Zhang, Xiaoming Zhai, Jionghao Lin +6
Large Language Model (LLM) agents are increasingly utilized in AI-aided education to support tutoring and learning. Effective communication strategies among LLM agents improve coll…
Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
Liang Zhang, Jionghao Lin, John Sabatini +6
Learner performance data collected by Intelligent Tutoring Systems (ITSs), such as responses to questions, is essential for modeling and predicting learners' knowledge states. Howe…
Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
Yoshee Jain, John Hollander, Amber He +3
Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing r…
A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education
Ehsan Latif, Yifan Zhou, Shuchen Guo +24
As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce…
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Liang Zhang, Jionghao Lin, John Sabatini +6
Learning performance data describe correct and incorrect answers or problem-solving attempts in adaptive learning, such as in intelligent tutoring systems (ITSs). Learning performa…
Generative Adversarial Networks for Imputing Sparse Learning Performance
Liang Zhang, Mohammed Yeasin, Jionghao Lin +2
Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners' progress a…
Integrating Attentional Factors and Spacing in Logistic Knowledge Tracing Models to Explore the Impact of Training Sequences on Category Learning
Meng Cao, Philip I. Pavlik, Wei Chu +1
In category learning, a growing body of literature has increasingly focused on exploring the impacts of interleaving in contrast to blocking. The sequential attention hypothesis po…
SPL: A Socratic Playground for Learning Powered by Large Language Model
Liang Zhang, Jionghao Lin, Ziyi Kuang +2
Dialogue-based Intelligent Tutoring Systems (ITSs) have significantly advanced adaptive and personalized learning by automating sophisticated human tutoring strategies within inter…
Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
Liang Zhang, Jionghao Lin, Conrad Borchers +4
Intelligent Tutoring Systems (ITSs) have significantly enhanced adult literacy training, a key factor for societal participation, employment opportunities, and lifelong learning. O…
3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
Liang Zhang, Jionghao Lin, Conrad Borchers +2
Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data c…