.png){kind=icon}
返回
Informative representations for forgetting-robust knowledge tracing
.png){kind=icon}
PRE
AI
DOI：10.1007/s11257-024-09391-4.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Tracing a student's knowledge state is critical for teaching and learning. Knowledge tracing aims to accurately predict student performance by analyzing historical records on online education platforms. Most studies have focused on a student's skill with interactions sequence to predict the probability of correctly answering the latest question. However, they still suffer from the challenge of information sparsity and student forgetting. Specifically, the relationship between question and skill, and the features related to question texts have not been integrated to enrich information exploration. Besides, modeling forgetting behavior remains a challenge in assessing a student's learning gains. In this paper, we present a novel model, namely Informative Representations for Forgetting-Robust Knowledge Tracing (IFKT). IFKT utilizes a light graph convolutional network to capture various relational structures via embedding propagation. Then, the embeddings are assembled with rich interaction features separately as the powerful representation. Furthermore, attention weights assignments are individualized using the relative positions, in addition to the relevance between the current question with historical interaction representations. Finally, we compare IFKT against seven knowledge tracing baselines on three real-world benchmark datasets, demonstrating the superiority of the proposed model.
Keyword:
Knowledge tracing
Graph
Interaction features
Attention
Forgetting
期刊
U
IF:
3.5
论文数:
535
被引数:
1.9K
机构
引用论文
.png){kind=icon}
Dynamic Changes in the Cerebellar-Interpositus/Red-Nucleus-Motoneuron Pathway During Motor Learning
PREAI
分享收藏Organizational Enablers of Data-Driven Digital Transformation: A Case Study from Banking Industry
PREAI
分享收藏
查看更多内容