Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns  

作　　者：P.N.Ramesh S.Kannimuthu 

机构地区：[1]Computer Science and Engineering,Karpagam Institute of Technology,Coimbatore,641105,India [2]Information Technology,Karpagam College of Engineering,Coimbatore,641032,India

出　　处：《Intelligent Automation & Soft Computing》2023年第3期3845-3860,共16页智能自动化与软计算（英文）

摘　　要：The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming.Since POJs have greater number of pro-gramming problems in their repository,learners experience information overload.Recommender systems are a common solution to information overload.Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difficulty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation patterns.Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and tofind learners’learning goals.Collaborativefiltering(CF)and con-tent-basedfiltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation patterns.The sequence similarity measure is used tofind the top k neighbors based on the sequence of problems solved by the learners.The experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

关 键 词：Recommender systems skill level navigation pattern programming online judge collaborativefiltering content-basedfiltering 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]