%0 Conference Paper %1 Barria-Pineda:2019:ENE:3314183.3323463 %A Barria-Pineda, Jordan %A Akhuseyinoglu, Kamil %A Brusilovsky, Peter %B International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data, ExHUM at the 27th ACM Conference On User Modelling, Adaptation And Personalization, UMAP '19 %C New York, NY, USA %D 2019 %I ACM %K e-learning explanation navigation-support recommender %P 273--277 %R 10.1145/3314183.3323463 %T Explaining Need-based Educational Recommendations Using Interactive Open Learner Models %U http://doi.acm.org/10.1145/3314183.3323463 %X Students might pursue different goals throughout their learning process. For example, they might be seeking new material to expand their current level of knowledge, repeating content of prior classes to prepare for an exam, or working on addressing their most recent misconceptions. Multiple potential goals require an adaptive e-learning system to recommend learning content appropriate for students' intent and to explain this recommendation in the context of this goal. In our prior work, we explored explainable recommendations for the most typical 'knowledge expansion goal". In this paper, we focus on students' immediate needs to remedy misunderstandings when they solve programming problems. We generate learning content recommendations to target the concepts with which students have struggled more recently. At the same time, we produce explanations for this recommendation goal in order to support students' understanding of why certain learning activities are recommended. The paper provides an overview of the design of this explainable educational recommender system and describes its ongoing evaluation %@ 978-1-4503-6711-0

@inproceedings{Barria-Pineda:2019:ENE:3314183.3323463, abstract = {Students might pursue different goals throughout their learning process. For example, they might be seeking new material to expand their current level of knowledge, repeating content of prior classes to prepare for an exam, or working on addressing their most recent misconceptions. Multiple potential goals require an adaptive e-learning system to recommend learning content appropriate for students' intent and to explain this recommendation in the context of this goal. In our prior work, we explored explainable recommendations for the most typical 'knowledge expansion goal". In this paper, we focus on students' immediate needs to remedy misunderstandings when they solve programming problems. We generate learning content recommendations to target the concepts with which students have struggled more recently. At the same time, we produce explanations for this recommendation goal in order to support students' understanding of why certain learning activities are recommended. The paper provides an overview of the design of this explainable educational recommender system and describes its ongoing evaluation}, acmid = {3323463}, added-at = {2019-06-26T18:37:58.000+0200}, address = {New York, NY, USA}, author = {Barria-Pineda, Jordan and Akhuseyinoglu, Kamil and Brusilovsky, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2cc1c26c1313a98f3712e6e8466e1b55b/brusilovsky}, booktitle = {International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data, ExHUM at the 27th ACM Conference On User Modelling, Adaptation And Personalization, UMAP '19}, description = {Explaining Need-based Educational Recommendations Using Interactive Open Learner Models}, doi = {10.1145/3314183.3323463}, interhash = {1566f95135f2933d4e40901287c9835b}, intrahash = {cc1c26c1313a98f3712e6e8466e1b55b}, isbn = {978-1-4503-6711-0}, keywords = {e-learning explanation navigation-support recommender}, location = {Larnaca, Cyprus}, numpages = {5}, pages = {273--277}, publisher = {ACM}, series = {UMAP'19 Adjunct}, timestamp = {2019-06-26T18:37:58.000+0200}, title = {Explaining Need-based Educational Recommendations Using Interactive Open Learner Models}, url = {http://doi.acm.org/10.1145/3314183.3323463}, year = 2019 }