PCKT: Problem Composition in Knowledge Tracking
Huaxiong Yao, Juntao Yang, Zuoquan Xie, Jia Guo, Renyi Chen, Mengling Hu · 2022
With the rapid development of online education, personalized education becomes more and more important. The emergence of knowledge tracking (KT) has made a significant breakthrough in personalized education. In recent years, researchers have achieved some good results by building the KT model to search information and relationships between problems and skills. However, in reality, problems are sometimes similar. The skills may be exactly the same between two problems, but only the data is different. Therefore, it is extremely important to take this into account when making predictions. To address this problem, we construct a Problem Composition Knowledge Tracking Model (PCKT). In PCKT, problems are devided into corresponding data and skills, and connected by certain relation. Experiments demonstrated our PCKT achieved state-of-the-art results on two public datasets.