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Paul Biberstein; Thomas Castleman; Luming Chen; Shriram Krishnamurthi – Informatics in Education, 2024
CODAP is a widely-used programming environment for secondary school data science. Its direct-manipulation-based design offers many advantages to learners, especially younger students. Unfortunately, these same advantages can become a liability when it comes to repeating operations consistently, replaying operations (for reproducibility), and also…
Descriptors: Data Science, Secondary School Students, Programming, Open Source Technology
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Marianthi Grizioti; Chronis Kynigos – Informatics in Education, 2024
Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying…
Descriptors: Computation, Thinking Skills, Data Science, Classification
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Gorjan Nadzinski; Branislav Gerazov; Stefan Zlatinov; Tomislav Kartalov; Marija Markovska Dimitrovska; Hristijan Gjoreski; Risto Chavdarov; Zivko Kokolanski; Igor Atanasov; Jelena Horstmann; Uros Sterle; Matjaz Gams – Informatics in Education, 2023
With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills…
Descriptors: Data Science, Artificial Intelligence, Man Machine Systems, Vocational Education