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 *** POJs have greater number of pro-gram...
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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 *** POJs have greater number of pro-gramming problems in their repository,learners experience information *** systems are a common solution to information *** 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 *** system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and tofind learners’learning ***filtering(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 *** sequence similarity measure is used tofind the top k neighbors based on the sequence of problems solved by 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.
The paper proposes a recommender system approach to cover onlinejudge's domains. onlinejudges are e-learning tools that support the automatic evaluation of programming tasks done by individual users, and for thi...
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The paper proposes a recommender system approach to cover onlinejudge's domains. onlinejudges are e-learning tools that support the automatic evaluation of programming tasks done by individual users, and for this reason they are usually used for training students in programming contest and for supporting basic programming teachings. The proposal pretends to suggest problems assuming that a user must try to solve those problems already successfully solved by similar users. With this goal, the authors adopt the traditional collaborative filtering method with a new similarity measure adapted to the current domain, and the authors propose several transformations in the user-problem matrix to incorporate specific onlinejudge's information. The authors evaluate the effect of the matrix configurations using Precision and Recall metrics, getting better results comparing with the authors method without transformations and with a representative state-of-art approach. Finally, the authors outline possible extensions to the current work.
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