Name
Abstract | ||
|---|---|---|
One of the fundamental tasks when providing personalized tutoring services to learners in online learning systems is to predict learner performance on future exercises. To achieve this goal, it is necessary to estimate and trace the knowledge proficiency (KP) of learners by modeling their learning performance. The existing models either fail to capture the long-term dependencies in the exercising sequence to model the influence of a previous exercise to the current one or find it difficult to explain the results. To solve these issues, we propose herein a novel model, called the knowledge interaction-enhanced knowledge tracing (KIKT), to estimate and trace the evolution of learners' KP. We first propose a framework by unifying the strength of the memory network to enhance the representation of the knowledge state and the interpretability of the Item Response Theory to explain learner performance. In this framework, we trace each learner's KP on each knowledge concept overtime, and further infer their proficiencies and the item characteristics using two kinds of neural networks. Moreover, we incorporate the knowledge interaction and the cognitive difficulty into our model to further exploit the long-term dependencies and the adaptive item difficulty in the exercising sequences. Extensive experiments conducted on five real-world datasets demonstrate the superiority of our model. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BESC51023.2020.9348285 | 2020 7th International Conference on Behavioural and Social Computing (BESC) |
Keywords | DocType | ISBN |
Learner Performance Prediction,Knowledge Tracing,Knowledge Interaction,Item Response Theory,Intelligent Tutoring System | Conference | 978-1-7281-8606-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenbin Gan | 1 | 0 | 2.03 |
| Yuan Sun | 2 | 0 | 1.01 |
| Yi Sun | 3 | 16 | 3.54 |