%0 Conference Paper %1 Machado_2021 %A Machado, Guilherme Medeiros %A Boyer, Anne %B Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization %D 2021 %I ACM %K sequencing umap2021 %P 95-99 %R 10.1145/3450614.3464481 %T Learning Path Recommender Systems: A Systematic Mapping %U https://doi.org/10.1145%2F3450614.3464481 %X Learning Path Recommender Systems (LPRS) are systems that make recommendations of learning resources to be consumed in a determined sequence. Such kind of recommendation is useful in scenarios where we need to personalize the learning especially when the students need to be guided faced an overwhelming amount of resources. LPRS are gaining more attention in the last years because of the popularity of e-learning, and such need to guide, motivate and engage students in big data scenarios. The systematic mapping proposed in this paper tries to understand how LPRS are done and how they are evaluated. Our findings suggest that the papers use mostly content-based algorithms and there is a lack of discussion on explainable and trustworthy LPRS.

@inproceedings{Machado_2021, abstract = {Learning Path Recommender Systems (LPRS) are systems that make recommendations of learning resources to be consumed in a determined sequence. Such kind of recommendation is useful in scenarios where we need to personalize the learning especially when the students need to be guided faced an overwhelming amount of resources. LPRS are gaining more attention in the last years because of the popularity of e-learning, and such need to guide, motivate and engage students in big data scenarios. The systematic mapping proposed in this paper tries to understand how LPRS are done and how they are evaluated. Our findings suggest that the papers use mostly content-based algorithms and there is a lack of discussion on explainable and trustworthy LPRS. }, added-at = {2021-10-13T17:09:49.000+0200}, author = {Machado, Guilherme Medeiros and Boyer, Anne}, biburl = {https://www.bibsonomy.org/bibtex/29151eea58c1e358ccc34e2ff64c649eb/brusilovsky}, booktitle = {Adjunct Proceedings of the 29th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Learning Path Recommender Systems: A Systematic Mapping | Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3450614.3464481}, interhash = {7be960548feae38b64da6acefaed79d9}, intrahash = {9151eea58c1e358ccc34e2ff64c649eb}, keywords = {sequencing umap2021}, month = jun, pages = {95-99}, publisher = {{ACM}}, timestamp = {2021-10-13T17:12:17.000+0200}, title = {Learning Path Recommender Systems: A Systematic Mapping}, url = {https://doi.org/10.1145%2F3450614.3464481}, year = 2021 }