Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/138988
Title: Extracting notional machines for databases
Authors: Miedema, Daphne
Fletcher, George
Aivaloglou, Fenia
Busuttil, Leonard
Farinetti, Laura
Goodfellow, Martin
Guerrini, Giovanna
Haldeman, Georgiana
Pan, Yuhan
Ramagoni, Sujeeth Goud
Satyavolu, Chandrika
Sooriamurthi, Raja
Tu, Xiaoying
Tudor, Liviana
Keywords: Education -- Databases
Computer science -- Data processing
Machine learning
Information visualization
Issue Date: 2025
Publisher: Association for Computing Machinery
Citation: Miedema, D., Fletcher, G., Aivaloglou, F., Busuttil, L., Farinetti, L., Goodfellow, M.,...Tudor, L. (2025, June). Extracting Notional Machines for Databases. Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education v. 2, Netherlands. 693-694.
Abstract: Database education is a cornerstone under many of the more popular topics in computer science such as machine learning and visualization. Although, in recent years, more fundamental research into database education has come out, there are many more ways in which it can be extended. Research on the practice of teaching databases, namely on the educational materials and explanations of teachers, can help us create new building blocks for fundamental research. This working group aims to collect and present notional machines of different types, for a wide range of database subtopics. These materials offer and updated context for database educators to design their courses from, as well as open up pathways of further research into database education.
URI: https://www.um.edu.mt/library/oar/handle/123456789/138988
Appears in Collections:Scholarly Works - FacEduTEE

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