Evidence map›Paper›PMID 34886245›Full record

ArticleInternational journal of environmental research and public health2021

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

Roberto Araya, Masami Isoda, Johan van der Molen Moris

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Roberto ArayaCenter for Advanced Research in Education, Institute of Education, Universidad de Chile, Santiago 8320000, Chile.ORCID 0000-0003-2598-8994
Masami IsodaFaculty of Humanities and Social Sciences, University of Tsukuba, Tsukuba 305-8577, Japan.
Johan van der Molen MorisMRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.

Funding

Agencia Nacional de Investigación y Desarrollo ANID/PIA/Basal Funds for Centers of Excellence FB0003Japan Society for the Promotion of Science Grant-in-Aid for Exploratory Research 19K21743
6 · The paper itself

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.

Indexed as

COVID-19PandemicsCurriculumHumansSARS-CoV-2SchoolsTeachingcomputational modelingcomputational thinkingCOVID-19lesson study

Identifiers

PMID34886245
PMCPMC8656684

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.