Developing Computational Thinking Teaching Strategies to Model Pandemics and Containment Measures.

Area of Science:

• Educational Technology
• Public Health
• Computational Science

Background:

• COVID-19 pandemic highlighted challenges in understanding virus transmission and controlling global health crises.
• There is a growing need to integrate computational thinking (CT) into Science, Technology, Engineering, and Mathematics (STEM) curricula.
• Existing STEM curricula lack practical examples applying CT to real-world problems like pandemic control.

Purpose of the Study:

• To propose and evaluate novel CT exemplars for pandemic modeling.
• To bridge the gap in applying CT to global health challenges within STEM education.
• To develop progressive learning activities across elementary and high school levels.

Main Methods:

• Designed CT exemplars based on the InMside project framework: algorithmic thinking, computational modeling, and machine learning.
• Developed a progressive sequence of activities for each CT pillar, suitable for various grade levels.
• Conducted an experimental study with elementary and middle school students from high-vulnerability schools to assess implementation and understanding.

Main Results:

• The computational modeling exemplar was successfully implemented by teachers.
• Students demonstrated correct understanding of the computational modeling concepts.
• The study confirmed the feasibility of introducing these CT exemplars across all grade levels.

Conclusions:

• It is feasible to integrate CT exemplars into STEM education for addressing real-world public health issues.
• These exemplars offer a powerful approach to STEM integration, enhancing students' understanding of complex problems.
• The developed CT activities support students in reflecting on and tackling significant public health challenges affecting their communities.