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Developing Computational Thinking to Help Tackle Pandemic Challenges

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COVID-19 has been extremely difficult to control. The lack of understanding of key aspects of pandemics has affected virus transmission. On the other hand, there is a demand to incorporate Computational Thinking (CT) in the curricula with applications in STEM. However, there are still no exemplars in the curriculum that apply CT to real-world problems such as controlling a pandemic or other similar global crises. In this paper, we fill this gap by proposing exemplars of CT for modeling the pandemic. We designed exemplars following the three pillars of the APEC InMside framework for CT: algorithmic thinking, computational modeling, and machine learning. For each pillar, we designed a progressive sequence of activities that covers from elementary to high school. In an experimental study with elementary and middle school students from 2 schools of high vulnerability, we found that the computational modeling exemplar can be implemented by teachers and correctly understood by students. We conclude that it is feasible to introduce the exemplars at all grade levels, and that this is a powerful example of STEM integration that helps reflect and tackle real-world and challenging public health problems of great impact for students and their families.
Title: Developing Computational Thinking to Help Tackle Pandemic Challenges
Description:
COVID-19 has been extremely difficult to control.
The lack of understanding of key aspects of pandemics has affected virus transmission.
On the other hand, there is a demand to incorporate Computational Thinking (CT) in the curricula with applications in STEM.
However, there are still no exemplars in the curriculum that apply CT to real-world problems such as controlling a pandemic or other similar global crises.
In this paper, we fill this gap by proposing exemplars of CT for modeling the pandemic.
We designed exemplars following the three pillars of the APEC InMside framework for CT: algorithmic thinking, computational modeling, and machine learning.
For each pillar, we designed a progressive sequence of activities that covers from elementary to high school.
In an experimental study with elementary and middle school students from 2 schools of high vulnerability, we found that the computational modeling exemplar can be implemented by teachers and correctly understood by students.
We conclude that it is feasible to introduce the exemplars at all grade levels, and that this is a powerful example of STEM integration that helps reflect and tackle real-world and challenging public health problems of great impact for students and their families.

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