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Tooee: A Novel Scratch Extension for K-12 Big Data and Artificial Intelligence Education using Text-based Visual Blocks
IEEE Access ; 2021.
Article in English | Scopus | ID: covidwho-1515158
ABSTRACT
Many approaches have been proposed to teach the basic concepts of big data and artificial intelligence to K-12 students based on block-based programming languages, such as Scratch. Using these approaches, young students can easily experience big data and artificial intelligence through a drag-and-drop approach. However, it remains difficult for them to perform more complex tasks, such as directly collecting data from the web or exploiting custom-made machine learning algorithms. In this paper, we propose a novel Scratch extension that allows Scratch to communicate with text-based programming languages such as Python and JavaScript using WebSockets. Unlike other Scratch extensions, our extension greatly enhances the extensibility of Scratch given its use of "text-based visual blocks" so that messages can be freely exchanged through a minimum number of blocks. In order for students to use these blocks easily, the blocks are designed such that they can be used as if talking with a friend named "Tooee." In order to show how this extension can help students create big data and artificial intelligence programs, we present eight example applications that students can easily implement. These are (1) Weather Forecast, (2) Top 5 Movies in Theaters, (3) COVID-19 Dashboard, (4) Saving Quiz Results to a CSV File, (5) Facial Image Classification, (6) Color Classification, (7) Object Classification, and (8) Handwriting Recognition. Our analyses and experimental results show that Tooee has several advantages over other educational environments. Author

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Access Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Access Year: 2021 Document Type: Article