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Computational Thinking (CT) towards Creative Action: Developing a Project-Based Instructional Taxonomy (PBIT) in AI Education.

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Název: Computational Thinking (CT) towards Creative Action: Developing a Project-Based Instructional Taxonomy (PBIT) in AI Education.
Autoři: Zhou, Chunfang, Zhang, Wei
Zdroj: Education Sciences; Feb2024, Vol. 14 Issue 2, p134, 14p
Témata: ARTIFICIAL intelligence, BLOOM'S taxonomy, LEARNING ability, DEEP learning, TAXONOMY
Geografický termín: CHINA
Korporace: NORTHEASTERN University (Boston, Mass.)
Abstrakt: This paper aims to develop a new model of Project-Based Instructional Taxonomy (PBIT) that provides a tool of course design that facilitates Computational Thinking (CT) development as creative action in solving real-life problems. Theoretically, PBIT is built on an integrative framework bringing together with studies on CT education, creativity, Bloom's Taxonomy, and Project-Based Instruction (PBI). This guides the course design to make alignment between diverse elements including education objectives, categories of CT, levels of learning ability, process of project facilitation, and methods of grading. A case will be discussed that focuses on a course Deep Learning and Technologies in AI bachelor program at Northeastern University (NEU) in China. It also shows how PBIT is applied in teaching practice that benefits students' CT development. As the conclusion indicates, this paper has contributions to future research on creativity, PBI, CT, and AI education. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:This paper aims to develop a new model of Project-Based Instructional Taxonomy (PBIT) that provides a tool of course design that facilitates Computational Thinking (CT) development as creative action in solving real-life problems. Theoretically, PBIT is built on an integrative framework bringing together with studies on CT education, creativity, Bloom's Taxonomy, and Project-Based Instruction (PBI). This guides the course design to make alignment between diverse elements including education objectives, categories of CT, levels of learning ability, process of project facilitation, and methods of grading. A case will be discussed that focuses on a course Deep Learning and Technologies in AI bachelor program at Northeastern University (NEU) in China. It also shows how PBIT is applied in teaching practice that benefits students' CT development. As the conclusion indicates, this paper has contributions to future research on creativity, PBI, CT, and AI education. [ABSTRACT FROM AUTHOR]
ISSN:22277102
DOI:10.3390/educsci14020134