Event Information
Blended Content1. Brief Introduction to Computational Thinking (5 minutes)
- Content: Overview of the four cornerstones: decomposition, pattern recognition, abstraction, and algorithms.
- Engagement: Quick introduction, framing computational thinking’s role in lesson design.
- Process: Use slides with key definitions and examples to help participants understand the core components of computational thinking.
2. Introduction of the Computational Thinking Problem (5 minutes)
- Problem: How do we design lessons that are relevant to the content we teach and engage all students in computational thinking practices?
- Content: Present the problem and explain how it relates to participants’ teaching contexts, encouraging them to consider how computational thinking can be integrated across subjects.
- Engagement: Participants reflect on their current lesson designs and share initial thoughts on integrating computational thinking.
Process: Brief discussion with the group to identify common challenges.
3. Decomposition of the Problem (10 minutes)
- Content: Guide participants in breaking down the problem into manageable parts (e.g., content relevance, student engagement, integrating computational thinking).
- Engagement: Small groups work together to decompose the problem, identifying essential elements for designing lessons that incorporate computational thinking.
- Process: Participants collaborate in small groups to decompose the problem and share their ideas with the larger group.
4. Data Analysis and Pattern Recognition (20 minutes)
- Content: Participants will sort through a set of 10 computational thinking lessons and categorize them according to the four computational thinking pillars: decomposition, pattern recognition, abstraction, and algorithms.
- Engagement: Hands-on activity where groups analyze the language and activities in each lesson to identify patterns in how each computational thinking component is taught.
- Process: Each group categorizes the lessons, identifies patterns in how each pillar is addressed, and discusses their findings. Group representatives then share insights with the whole room in a brief discussion.
5. Development of an Algorithm (10 minutes)
- Content: Based on the identified patterns, participants develop a simple algorithm or step-by-step plan to create lessons that integrate computational thinking pillars effectively.
- Engagement: Groups work together to outline a process for designing computational thinking lessons using the patterns they identified.
- Process: Groups create their algorithms, share them with the larger group, and receive feedback on their approaches.
6. Leveraging AI for Lesson Design (5 minutes)
- Content: Discuss how AI can support the creation of computational thinking lessons, showing how AI tools can help design engaging lessons or generate new ideas.
- Engagement: A quick demonstration of AI tools like ChatGPT or AI-driven lesson planning software.
- Process: Demonstration followed by a brief discussion on the role of AI in lesson design and how it can complement computational thinking practices.
7. Review of Resources & Q&A (5 minutes)
- Content: Provide participants with resources to explore computational thinking and AI further.
- Engagement: Open floor for final questions and a quick review of key resources shared during the session.
- Process: Distribute resources and address any remaining questions from participants.
- ISTE Standards - Computational Thinking
- Choi, S., Jang, Y., & Kim, K. (2023), Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human-Computer Interaction, 39(4), 910–922. https://doi.org/10.1080/10447318.2022.2049145
- Bryant, J., Heitz, C., Sanghvi, S., & Wagle, D. (2020). How artificial intelligence will impact K-12 teachers. McKinsey. https://www.mckinsey.com/industries/education/our-insights/how-artificial-intelligence-will-impact-k-12-teachers
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