.png){kind=icon}
Return
Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2022.3206447.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Tracing a student's knowledge is vital for tailoring the learning experience. Recent knowledge tracing methods tend to respond to these challenges by modelling knowledge state dynamics across learning concepts. However, they still suffer from several inherent challenges including: modelling forgetting behaviors and identifying relationships among latent concepts. To address these challenges, in this paper, we propose a novel knowledge tracing model, namely Deep Graph Memory Network (DGMN). In this model, we incorporate a forget gating mechanism into an attention memory structure in order to capture forgetting behaviors dynamically during the knowledge tracing process. Particularly, this forget gating mechanism is built upon attention forgetting features over latent concepts considering their mutual dependencies. Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states. A comprehensive experimental evaluation has been conducted using four well-established benchmark datasets. The results show that DGMN consistently outperforms the state-of-the-art KT models over all the datasets. The effectiveness of modelling forgetting behaviors and learning latent concept graphs has also been analyzed in our experiments.
Keywords:
Deep learning
graph
memory networks
knowledge tracing
forgetting
Journal
IF:
10.4
Papers:
6.9K
Citations:
3.2W
Organization
Cited Papers
ShareSaveDramatic reduction in sperm parameters following bariatric surgery: report of two cases.png)
Andrologia
AndrologiaIF0
PREAI
ShareSave
ShareSaveShareSaveHierarchical temporal memory and recurrent neural networks for time series prediction: An empirical validation and reduction to multilayer perceptronsNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSave
View more