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

P. N. Ramesh1,*, S. Kannimuthu2

1 Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore, 641105, India
2 Information Technology, Karpagam College of Engineering, Coimbatore, 641032, India

* Corresponding Author: P. N. Ramesh. Email: email

Intelligent Automation & Soft Computing 2023, 35(3), 3845-3860.


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 navigation patterns. Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and to find learners’ learning goals. Collaborative filtering (CF) and content-based filtering approaches are employed to recommend problems in the current and next skill levels based on frequent skill level navigation patterns. The sequence similarity measure is used to find 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’ current skill level and learning goals outperforms the other approaches in practice problem recommender systems.


Cite This Article

P. N. Ramesh and S. Kannimuthu, "Context-aware practice problem recommendation using learners’ skill level navigation patterns," Intelligent Automation & Soft Computing, vol. 35, no.3, pp. 3845–3860, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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