no code implementations • 4 Mar 2024 • Liang Zhang, Jionghao Lin, Conrad Borchers, John Sabatini, John Hollander, Meng Cao, Xiangen Hu
This research is motivated by the potential of LLMs to predict learning performance based on its inherent reasoning and computational capabilities.
no code implementations • 29 Jan 2024 • Liang Zhang, Jionghao Lin, Conrad Borchers, Meng Cao, Xiangen Hu
Learning performance data (e. g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level.
no code implementations • 8 Aug 2023 • Richard Jiarui Tong, Cassie Chen Cao, Timothy Xueqian Lee, Guodong Zhao, Ray Wan, FeiYue Wang, Xiangen Hu, Robin Schmucker, Jinsheng Pan, Julian Quevedo, Yu Lu
This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents.
no code implementations • 23 Jun 2022 • Shuyan Huang, Qiongqiong Liu, Jiahao Chen, Xiangen Hu, Zitao Liu, Weiqi Luo
We propose a simple but effective method to recommend exercises with high quality and diversity for students.
no code implementations • 12 Feb 2016 • Tai Wang, Xiangen Hu, Keith Shubeck, Zhiqiang Cai, Jie Tang
The relationship between reading and writing (RRW) is one of the major themes in learning science.