Jordan Barria-Pineda
Kamil Akhuseyinoglu
Peter Brusilovsky
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
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- Jordan Barria-Pineda and Peter Brusilovsky. 2019. Making Educational Recommendations Transparent through a Fine-Grained Open Learner Model. In Joint Proceedings of the ACM IUI 2019 Workshops co-located with the 24th ACM Conference on Intelligent User Interfaces (ACM IUI 2019), Los Angeles, USA, March 20, 2019.Google Scholar
- Roya Hosseini and Peter Brusilovsky. 2017. A Study of Concept-Based Similarity Approaches for Recommending Program Examples. New Review of Hypermedia and Multimedia 23, 3 (2017), 161--188.Google ScholarCross Ref
- Roya Hosseini, Peter Brusilovsky, and Julio Guerra. 2013. Knowledge Maximizer: Concept-based Adaptive Problem Sequencing for Exam Preparation. In the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (LNAI), Vol. 7926. 848--851.Google Scholar
- Roya Hosseini, I-Han Hsiao, Julio Guerra, and Peter Brusilovsky. 2015. What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling. In Design for Teaching and Learning in a Networked World, Gráinne Conole, Toma? Klobu?ar, Christoph Rensing, Johannes Konert, and Elise Lavoué (Eds.). Springer International Publishing, Cham, 155--168.Google Scholar
- Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, and Lise Getoor. 2019. Personalized explanations for hybrid recommender systems. In the 24th International Conference on Intelligent User Interfaces (IUI '19). ACM, 379--390.Google ScholarDigital Library
- Tomasz D Loboda, Julio Guerra, Roya Hosseini, and Peter Brusilovsky. 2014. Mastery grids: An open source social educational progress visualization. In European conference on technology enhanced learning. Springer, 235--248. Google ScholarDigital Library
- Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, and Erik Duval. 2013. Recommender Systems for Learning. Springer, Berlin. http://www.springer.com/ us/book/9781461443605 Google Scholar
- Bonnie M Muir. 1994. Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics 37, 11 (1994), 1905--1922.Google ScholarCross Ref
- Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2016. ExpLOD: A Framework for Explaining Recommendations Based on the Linked Open Data Cloud. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 151--154.Google ScholarDigital Library
- Vanessa Putnam and Cristina Conati. 2019. Exploring the Need for Explainable Artificial Intelligence (XAI) in Intelligent Tutoring Systems (ITS). In Joint Proceedings of the ACM IUI 2019 Workshops co-located with the 24th ACM Conference on Intelligent User Interfaces (ACM IUI 2019), Los Angeles, USA, March 20, 2019.Google Scholar
- Masahiro Sato, Budrul Ahsan, Koki Nagatani, Takashi Sonoda, Qian Zhang, and Tomoko Ohkuma. 2018. Explaining Recommendations Using Contexts. In 23rd International Conference on Intelligent User Interfaces. ACM, 659--664. Google ScholarDigital Library
- Nava Tintarev and Judith Masthoff. 2011. Designing and Evaluating Explanations for Recommender Systems. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). Springer US, Boston, MA, 479--510.Google Scholar
- Michael Yudelson, Peter Brusilovsky, and Vladimir Zadorozhny. 2007. A User Modeling Server for Contemporary Adaptive Hypermedia: An Evaluation of the Push Approach to Evidence Propagation. In User Modeling 2007, Cristina Conati, Kathleen McCoy, and Georgios Paliouras (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 27--36. Google ScholarDigital Library
Index Terms
- Explaining Need-based Educational Recommendations Using Interactive Open Learner Models
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