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.
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.
1 code implementation • 29 Jan 2021 • Mengfan Yao, Siqian Zhao, Shaghayegh Sahebi, Reza Feyzi Behnagh
Hawkes processes have been shown to be efficient in modeling bursty sequences in a variety of applications, such as finance and social network activity analysis.
no code implementations • 29 Jan 2021 • Mengfan Yao, Siqian Zhao, Shaghayegh Sahebi, Reza Feyzi Behnagh
However, previous attempts on dynamic modeling of student procrastination suffer from major issues: they are unable to predict the next activity times, cannot deal with missing activity history, are not personalized, and disregard important course properties, such as assignment deadlines, that are essential in explaining the cramming behavior.
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.