1 code implementation • 26 Jan 2023 • Siqian Zhao, Chunpai Wang, Shaghayegh Sahebi
TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities.
no code implementations • 6 Oct 2022 • Fei Jie, Chunpai Wang, Feng Chen, Lei LI, Xindong Wu
We propose a generalized framework for block-structured nonconvex optimization, which can be applied to structured subgraph detection in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others.
1 code implementation • 6 Oct 2022 • Chunpai Wang, Shaghayegh Sahebi, Siqian Zhao, Peter Brusilovsky, Laura O. Moraes
In this paper, we argue that not all attempts are equivalently important in discovering students' knowledge state, and some attempts can be summarized together to better represent student performance.
no code implementations • 26 Jun 2022 • Chunpai Wang, Daniel B. Neill, Feng Chen
We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs.
1 code implementation • 23 Jun 2020 • Siqian Zhao, Chunpai Wang, Shaghayegh Sahebi
In this paper, we propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types while unveiling the association between the learning materials of different types.