Development of a Pandemic Awareness STEM Outreach Curriculum: Utilizing a Computational Thinking Taxonomy Framework
Saved in:
| Title: | Development of a Pandemic Awareness STEM Outreach Curriculum: Utilizing a Computational Thinking Taxonomy Framework |
|---|---|
| Authors: | Gilchrist, Pamela O., Alexander, Alonzo B. (ORCID |
| Source: | Education Sciences. 2021 11. |
| Availability: | MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. Tel: e-mail: indexing@mdpi.com; Web site: http://www.mdpi.com |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2021 |
| Sponsoring Agency: | National Institutes of Health (DHHS) |
| Contract Number: | RO1CA571340 |
| Document Type: | Journal Articles Reports - Descriptive |
| Education Level: | Junior High Schools Middle Schools Secondary Education |
| Descriptors: | Science Education, Science Curriculum, STEM Education, Informal Education, Outreach Programs, Curriculum Development, COVID-19, Pandemics, Computation, Thinking Skills, Taxonomy, Middle School Students, Epidemiology, Scientific Concepts, Problem Solving, Hands on Science, Curriculum Design, Teaching Methods |
| Geographic Terms: | North Carolina |
| ISSN: | 2227-7102 |
| Abstract: | Computational thinking is an essential skill in the modern global workforce. The current public health crisis has highlighted the need for students and educators to have a deeper understanding of epidemiology. While existing STEM curricula has addressed these topics in the past, current events present an opportunity for new curricula that can be designed to present epidemiology, the science of public health, as a modern topic for students that embeds the problem-solving and mathematics skills of computational thinking practices authentically. Using the Computational Thinking Taxonomy within the informal education setting of a STEM outreach program, a curriculum was developed to introduce middle school students to epidemiological concepts while developing their problem-solving skills, a subset of their computational thinking and mathematical thinking practices, in a contextually rich environment. The informal education setting at a Research I Institution provides avenues to connect diverse learners to visually engaging computational thinking and data science curricula to understand emerging teaching and learning approaches. This paper documents the theory and design approach used by researchers and practitioners to create a Pandemic Awareness STEM Curriculum and future implications for teaching and learning computational thinking practices through engaging with data science. |
| Abstractor: | As Provided |
| Entry Date: | 2021 |
| Accession Number: | EJ1290327 |
| Database: | ERIC |
| Abstract: | Computational thinking is an essential skill in the modern global workforce. The current public health crisis has highlighted the need for students and educators to have a deeper understanding of epidemiology. While existing STEM curricula has addressed these topics in the past, current events present an opportunity for new curricula that can be designed to present epidemiology, the science of public health, as a modern topic for students that embeds the problem-solving and mathematics skills of computational thinking practices authentically. Using the Computational Thinking Taxonomy within the informal education setting of a STEM outreach program, a curriculum was developed to introduce middle school students to epidemiological concepts while developing their problem-solving skills, a subset of their computational thinking and mathematical thinking practices, in a contextually rich environment. The informal education setting at a Research I Institution provides avenues to connect diverse learners to visually engaging computational thinking and data science curricula to understand emerging teaching and learning approaches. This paper documents the theory and design approach used by researchers and practitioners to create a Pandemic Awareness STEM Curriculum and future implications for teaching and learning computational thinking practices through engaging with data science. |
|---|---|
| ISSN: | 2227-7102 |
