Developing Computational Thinking Teaching Strategies to Model Pandemics and Containment Measures

Abstract

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 framework for CT from the Inclusive Mathematics for Sustainability in a Digital Economy (InMside) project by Asia-Pacific Economic Cooperation (APEC): 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 Science Technology, Engineering, and Mathematics (STEM) integration that helps reflect and tackle real-world and challenging public health problems of great impact for students and their families.

Habilidades del siglo XXI y Socioepistemología. Homenaje a Ricardo Cantoral

“…Por otra parte, la desigualdad tiene, ahora, una cara diferente. Las aplicaciones, apps, simplifican la vida; sin embargo, están reemplazando a muchas personas, y toman ya un protagonismo preocupante para la confianza pública y el control de la propia individualidad (ARAYA; ISODA; VAN DER MOLEN MORIS, 2021).…”

Teaching Machine Learning to Middle and High School Students from a Low Socio-Economic Status Background

“…The courses we encountered are typically aimed at novices with short durations ranging from 3 (Zhang et al, 2022) to 6 weeks (Everson et al, 2022). Courses are applied face-to-face (Araya et al, 2021) or remotely (Zhang et al, 2022;Everson et al, 2022). In this context, various pedagogical approaches are adopted including collaborative learning (Araya et al, 2021;Everson et al, 2022), interactive and game-based learning (Zhang et al, 2022).…”

“…Courses are applied face-to-face (Araya et al, 2021) or remotely (Zhang et al, 2022;Everson et al, 2022). In this context, various pedagogical approaches are adopted including collaborative learning (Araya et al, 2021;Everson et al, 2022), interactive and game-based learning (Zhang et al, 2022). While Eguchi (2021) and Zhang et al (2022) use Google Teachable Machine as a tool, Everson et al (2022) used an AI chatbot created by the students themselves.…”

“…While Eguchi (2021) and Zhang et al (2022) use Google Teachable Machine as a tool, Everson et al (2022) used an AI chatbot created by the students themselves. Others due to limitations related to the technical infrastructure apply only unplugged activities (Araya et al, 2021). Learning is assessed based on the students' performance (Everson et al, 2022;Zhang et al, 2022) or through tests (Araya et al, 2021).…”

“…Others due to limitations related to the technical infrastructure apply only unplugged activities (Araya et al, 2021). Learning is assessed based on the students' performance (Everson et al, 2022;Zhang et al, 2022) or through tests (Araya et al, 2021). The findings suggest that students in underprivileged contexts can comprehend computational models, reflect on their limitations, and understand AI/ML concepts, such as artificial neural networks and supervised learning, as well as even complex concepts, including Generative Adversarial Networks (GANs) (Martins and Gresse von Wangenheim, 2023).…”