P. N. Ramesh1,*, S. Kannimuthu2
Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329
Abstract 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 programming 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… More >
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