Personalized Remedial Recommendations for SQL Programming Practice System

Jordan Barria-Pineda, Kamil Akhuseyinoglu, Peter Brusilovsky, Kerttu Pollari-Malmi, Teemu Sirkiä, Lauri Malmi

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Personalized recommendation of learning content is one of the most frequently cited benefits of personalized online learning. It is expected that with personalized content recommendation students will be able to build their own unique and optimal learning paths and to achieve course goals in the most optimal way. However, in many practical cases students search for learning content not to expand their knowledge, but to address problems encountered in the learning process, such as failures to solve a problem. In these cases, students could be better assisted by remedial recommendations focused on content that could help in resolving current problems. This paper presents a transparent and explainable interface for remedial recommendations in an online programming practice system. The interface was implemented to support SQL programming practice and evaluated in the context of a large database course. The paper summarizes the insights obtained from the study and discusses future work on remedial recommendations.

Original languageEnglish
Title of host publicationUMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
Number of pages8
ISBN (Electronic)9781450367110
Publication statusPublished - 14 Jul 2020
MoE publication typeA4 Article in a conference publication
EventConference on User Modeling, Adaptation and Personalization - Online, Genoa, Italy
Duration: 14 Jul 202017 Jul 2020
Conference number: 28


ConferenceConference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP


  • educational recommender systems
  • explainability
  • transparency


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