Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleJuly 2024
Learning path recommendation with multi-behavior user modeling and cascading deep Q networks
Knowledge-Based Systems (KNBS), Volume 294, Issue CJun 2024https://doi.org/10.1016/j.knosys.2024.111743AbstractAn online learning platform has become an important channel for learners to obtain knowledge due to its easy access and rich resources. In order to meet online learners' short-term needs with frequent changes and long-term learning goals during ...
- research-articleApril 2024
Doubly constrained offline reinforcement learning for learning path recommendation
Knowledge-Based Systems (KNBS), Volume 284, Issue CJan 2024https://doi.org/10.1016/j.knosys.2023.111242AbstractLearning path recommendation refers to the application of interactive recommendation systems in the field of education, aimed at optimizing learning outcomes while minimizing the workload of learners, teachers, and curriculum designers. ...
Highlights- We apply offline reinforcement learning to the task of learning path recommendation.
- Our model handls the extrapolation error in RL within educational settings.
- The performance of the RL-based system is influenced by the simulated ...
- research-articleSeptember 2023
A fine-grained and multi-context-aware learning path recommendation model over knowledge graphs for online learning communities
Information Processing and Management: an International Journal (IPRM), Volume 60, Issue 5Sep 2023https://doi.org/10.1016/j.ipm.2023.103464Highlights- A multidimensional knowledge graph is designed to represent knowledge related entities and characteristic attributes.
- A fine-grained and multi-context-aware learning path recommendation model for online communities is proposed.
- The ...
Existing approaches to learning path recommendation for online learning communities mainly rely on the individual characteristics of users or the historical records of their learning processes, but pay less attention to the semantics of users’ ...
- ArticleJuly 2023
A Personalized Learning Path Recommendation Method for Learning Objects with Diverse Coverage Levels
Artificial Intelligence in EducationJul 2023, Pages 714–719https://doi.org/10.1007/978-3-031-36272-9_61AbstractE-learning has resulted in the proliferation of educational resources, but challenges remain in providing personalized learning materials to learners amidst an abundance of resources. Previous personalized learning path recommendation (LPR) ...
- research-articleMay 2023
Knowledge graph for recommendation system: enhanced relation reliability and prediction probability (ERRaPP)
Multimedia Tools and Applications (MTAA), Volume 83, Issue 2Jan 2024, Pages 3525–3546https://doi.org/10.1007/s11042-023-15790-3AbstractWith the current explosion of information, the end-users find it challenging to filter this information. Recommendation systems present solutions to filter and prioritize the information to overcome the problem of information overloading. However, ...
- ArticleJuly 2022
Learning Optimal and Personalized Knowledge Component Sequencing Policies
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral ConsortiumJul 2022, Pages 338–342https://doi.org/10.1007/978-3-031-11647-6_65AbstractOne of the goals of adaptive learning systems is to realize adaptive learning sequencing by optimizing the order of learning materials to be presented to different learners. This paper proposes a novel approach to recommending optimal and ...
- ArticleJuly 2022
Managing Learners’ Memory Strength in a POMDP-Based Learning Path Recommender System
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral ConsortiumJul 2022, Pages 284–288https://doi.org/10.1007/978-3-031-11647-6_53AbstractThis paper views the learning path recommendation task as a sequential decision problem and considers Partially Observable Markov Decision Process (POMDP) as an adequate approach. This work proposes M-POMDP, a POMDP-based recommendation model that ...
- ArticleAugust 2021
Learning Path Recommendation for MOOC Platforms Based on a Knowledge Graph
Knowledge Science, Engineering and Management Aug 2021, Pages 600–611https://doi.org/10.1007/978-3-030-82147-0_49AbstractWith the development of Internet technologies and the increasing demand for knowledge, increasingly more people choose online learning platforms as a way to acquire knowledge. However, the rapid growth in the types and number of courses makes it ...
- ArticleSeptember 2020
New Measures for Offline Evaluation of Learning Path Recommenders
Addressing Global Challenges and Quality EducationSep 2020, Pages 259–273https://doi.org/10.1007/978-3-030-57717-9_19AbstractRecommending students useful and effective learning paths is highly valuable to improve their learning experience. The evaluation of the effectiveness of this recommendation is a challenging task that can be performed online or offline. Online ...
- research-articleSeptember 2020
LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory
Education and Information Technologies (KLU-EAIT), Volume 25, Issue 5Sep 2020, Pages 3797–3819https://doi.org/10.1007/s10639-020-10133-3AbstractThe educational community has been interested in personalized learning systems that can adapt itself while providing learning support to different learners to overcome the weakness of ‘one size fits all’ approaches in technology-enabled learning ...
- research-articleOctober 2019
An approach to helping developers learn open source projects based on machine learning
Internetware '19: Proceedings of the 11th Asia-Pacific Symposium on InternetwareOctober 2019, Article No.: 13, Pages 1–10https://doi.org/10.1145/3361242.3361251Developers usually learn excellent coding methods and design patterns by reading the code from well-known open-source projects, and participate in the development of open-source projects to enhance their programming capabilities. When developers have ...