Personalized Remedial Recommendations for SQL Programming Practice System
| Autor: | Teemu Sirkiä, Lauri Malmi, Jordan Barria-Pineda, Peter Brusilovsky, Kerttu Pollari-Malmi, Kamil Akhuseyinoglu |
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| Přispěvatelé: | University of Pittsburgh, Department of Computer Science, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
transparency
SQL Process (engineering) Interface (Java) Computer science Online learning Context (language use) 02 engineering and technology Transparency (human–computer interaction) Engineering management Work (electrical) 020204 information systems explainability 0202 electrical engineering electronic engineering information engineering educational recommender systems 020201 artificial intelligence & image processing Remedial education computer computer.programming_language |
| Zdroj: | UMAP (Adjunct Publication) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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