Prediction of Online Judge Practice Passing Rate Based on Knowledge Tracing

• 作者: HUANG Yongfeng CHENG Yanhua
• 来源期刊: 东华大学学报（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Artificial Intelligence and Communication Technology
• 页码范围: 240-244
• 页数: 5页
• 分类号: TP3|G633.67
• 语种: 英文
• DOI: 10.19884/j.1672-5220.202011091

摘要

Programming ability has become one of the most practical basic skills,and it is also the foundation of software development.However,in the daily training experiment,it is difficult for students to find suitable exercises from a large number of topics provided by numerous online judge (OJ) systems.Recommending high passing rate topics with an effective prediction algorithm can effectively solve the problem.Directly applying some common prediction algorithms based on knowledge tracing could bring some problems,such as the lack of the relationship among programming exercises and dimension disaster of input data.In this paper,those problems were analyzed,and a new prediction algorithm was proposed.Additional information,which represented the relationship between exercises,was added in the input data.And the input vector was also compressed to solve the problem of dimension disaster.The experimental results show that deep knowledge tracing (DKT) with side information and compression (SC) model has an area under the curve(AUC) of 0.7761,which is better than other models based on knowledge tracing and runs faster.