ReturnPreparing Students for the Future: Integrating Computational Thinking in K-12 Curriculum
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Colorado Convention Center, Bluebird Ballroom Lobby, Table 21
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Session description
Purpose & objective
Computational thinking is now part of the CS curriculum and should be appropriately covered in all grades.
Teachers who want to teach coding and/or robotics will understand the importance of graded lesson plans and resources. The curriculum must follow a spiral, project-based approach based on the age and school grade of the students.
They will learn how to choose coding tools and educational robots based on the time available per week, the students' existing skills, and the current trends in CS education.
Outline
The importance of computational thinking and coding/robotics in the current education landscape
- Introduction to the Digital Kids/Teens Second Edition Curriculum
- Explanation of how the curriculum supports computational thinking, coding, and robotics
- Discussion on the age-appropriate and graded nature of the curriculum
- How the curriculum aligns with ISTE and other international standards
- Introduction to the lesson plans for teachers with no prior knowledge
- Overview of the Can Code and Robotics ebooks
- Explanation of how the curriculum supports inclusivity and diversity
- Discussion on the dynamic nature of technology education
- How the curriculum is designed to be future-proof and adaptable
- Time for the audience to ask questions
- Call-to-action (encouraging attendees to try the curriculum in their own educational settings)
Supporting research
The Digital Kids and Digital Teens curriculum is field-tested in over 60 countries. The feedback we have received from teachers and students is integrated into the second edition of the series. Customized editions of the material are reviewed, approved and used by Ministries of Education worldwide for the subject of Computing and ICT.
Session specifications
Computational Thinker
- Students formulate problem definitions suited for technology-assisted methods such as data analysis, abstract models and algorithmic thinking in exploring and finding solutions.
- Students collect data or identify relevant data sets, use digital tools to analyze them, and represent data in various ways to facilitate problem-solving and decision-making.
- Students break problems into component parts, extract key information, and develop descriptive models to understand complex systems or facilitate problem-solving.