%0 Conference Paper %1 Yu_2021 %A Yu, Run %A Pardos, Zach %A Chau, Hung %A Brusilovsky, Peter %B Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization %D 2021 %I ACM %K UMAP2021 course-recommender explanation myown recommender user-study %P 238-245 %R 10.1145/3450614.3464483 %T Orienting Students to Course Recommendations Using Three Types of Explanation %U https://doi.org/10.1145%2F3450614.3464483 %X An emerging challenge in course recommender systems is explaining to students why they have been recommended particular courses. In the context of a university, it can be valuable for a recommender system to introduce students to courses they may have not otherwise taken but which are still relevant to them. However, there is a tension between these goals as students have less ability to judge the relevance of courses, the less familiar they are with them. In this paper, we explore ways of familiarizing students with recommendations with three types of explanations designed with varying levels of personalization. We conduct a 67 student randomized controlled experiment using two course recommendation engines, content and context-based, and augment recommendations with three different types of explanation. Students rated each course recommended in terms of novelty, unexpectedness, and successfulness (i.e., intent to enroll). We find several statistically significant results, including an increase in serendipity (i.e., unexpectedness + successfulness) when explaining new course recommendations using keywords from courses a student has previously taken.

@inproceedings{Yu_2021, abstract = {An emerging challenge in course recommender systems is explaining to students why they have been recommended particular courses. In the context of a university, it can be valuable for a recommender system to introduce students to courses they may have not otherwise taken but which are still relevant to them. However, there is a tension between these goals as students have less ability to judge the relevance of courses, the less familiar they are with them. In this paper, we explore ways of familiarizing students with recommendations with three types of explanations designed with varying levels of personalization. We conduct a 67 student randomized controlled experiment using two course recommendation engines, content and context-based, and augment recommendations with three different types of explanation. Students rated each course recommended in terms of novelty, unexpectedness, and successfulness (i.e., intent to enroll). We find several statistically significant results, including an increase in serendipity (i.e., unexpectedness + successfulness) when explaining new course recommendations using keywords from courses a student has previously taken. }, added-at = {2021-07-12T18:06:20.000+0200}, author = {Yu, Run and Pardos, Zach and Chau, Hung and Brusilovsky, Peter}, biburl = {https://www.bibsonomy.org/bibtex/256332ffce21cd3d8873b34268bc7cc3c/brusilovsky}, booktitle = {Adjunct Proceedings of the 29th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Orienting Students to Course Recommendations Using Three Types of Explanation | Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3450614.3464483}, interhash = {da63961c5f5ce89e14becbb511898c73}, intrahash = {56332ffce21cd3d8873b34268bc7cc3c}, keywords = {UMAP2021 course-recommender explanation myown recommender user-study}, month = jun, pages = {238-245}, publisher = {{ACM}}, timestamp = {2023-06-05T00:23:08.000+0200}, title = {Orienting Students to Course Recommendations Using Three Types of Explanation}, url = {https://doi.org/10.1145%2F3450614.3464483}, year = 2021 }