The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data hunches . We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.

Data Hunches: Incorporating Personal Knowledge into Visualizations

Current environmental problems are the primary focus for environmental science students and researchers. Sustainable environmental solutions require interdisciplinary thought processes, which pose difficulty to both students and the public. Computational thinking is an emerging term emphasized by progressive science curricula. Computational thinking and environmental science are both interdisciplinary by nature. Learning about sustainable environmental solutions requires students to partake in computational thinking. These ideas lend toward an expansive learning progression that encourages scaffolded and differentiated student progress in both computational knowledge and environmental knowledge. The learning progression, which emerges from the conceptual framework, emphasizes the spheres of sustainability, research, education, and economic perspectives to support environmental science learning through computational thinking. Computational thinking emphasized by the computational components (input, integration, output, and feedback) support learning about environmental solutions within the learning progression. The learning progression promotes application and implications for educators, students, researchers, and environmental scientists. 

/pdf/computational-thinking-to-learn-environmental-sustainability-13g6cem8.pdf

Computational Thinking to Learn Environmental Sustainability: A Learning Progression

In our increasingly technology-dependent society, the importance of promoting digital literacy (e.g., computational thinking, coding, and programming) has become a critical focus in the field of childhood education. While young children these days are routinely and extensively exposed to digital devices and tools, the efficacy of the methods for fostering digital skills in the early childhood classroom has not always been closely considered. This is particularly true in settings where early childhood educators are not digital experts. Currently, most of the efforts in standard early childhood settings, taught by teachers who are not digital experts, appear to revolve around “unplugged” activities that do not directly involve digital tools or devices, and it is not entirely clear how well these “unplugged” lessons promote the corresponding skills in “plugged” settings, such as coding and programming. This article discusses how, through further research, we may be able to devise an effective method for seamlessly building digital literacy among young children, transcending the “unplugged vs. plugged” barriers effortlessly.

/pdf/computational-thinking-and-coding-for-young-children-a-8070n339.pdf

Computational Thinking and Coding for Young Children: A Hybrid Approach to Link Unplugged and Plugged Activities

El pensamiento crítico constituye un elemento esencial en la alfabetización científica cuya importancia ha cobrado un mayor peso en la era de la post-verdad. Invertir esfuerzos en generar diseños didácticos para la práctica de pensamiento crítico es un objetivo esencial de la investigación actual en didáctica de las ciencias. En este artículo se sugiere incorporar un enfoque de enseñanza de pensamiento crítico que involucre la acción en la que se combinen distintos elementos de la práctica y de la metacognición. En base a los resultados de un proyecto sobre pensamiento crítico en la enseñanza de ciencias se presenta: 1) un análisis de las distintas visiones/concepciones sobre pensamiento crítico y sus implicaciones para la enseñanza; 2) argumentos para el desarrollo de pensamiento crítico en contextos relacionados con la post-verdad y principales desafíos; 3) nuestra visión sobre la enseñanza de pensamiento crítico y claves para su aplicación en el aula y, finalmente, 4) un ejemplo de diseño para la práctica de pensamiento crítico fruto del trabajo conjunto de investigadoras/es y docentes de secundaria. Palabras clave: Pensamiento crítico, era de la post-verdad, problemáticas socio-científicas, enseñanza de ciencias. Integrating Critical thinking in Science Education at the Post-truth Era Abstract: Critical thinking is an essential component of scientific literacy that has raised its importance in the Post-truth Era. Investing efforts in developing teaching designs for the practice of critical thinking is a main goal of current research in science education. This article suggests integrating a critical thinking teaching approach that involves action in which elements of the practice and metacognition combine. Based on the results of a project on critical thinking in science instruction, we present: 1) an analysis of the different views/conceptions of critical thinking and their teaching implications; 2) arguments for critical thinking development in contexts related to post-truth era and the main challenges; 3) our vision about critical thinking instruction and key points for its application in the classroom; 4) an example of design for the practice of critical thinking developed as a result of the joint work of researchers and secondary school teachers. Keywords: Critical thinking, post-truth era, socio-scientific issues, science education. 

Integrar el Pensamiento Crítico en la Educación Científica en la Era de la Post-verdad

Abstract The role of computation in science is ever‐expanding and is enabling scientists to investigate complex phenomena in more powerful ways and tackle previously intractable problems. The growing role of computation has prompted calls to integrate computational thinking (CT) into science instruction in order to more authentically mirror contemporary science practice and to support inclusive engagement in science pathways. In this multimethods study, we present evidence for the Computational Thinking for Science (CT+S) instructional model designed to support broader participation in science, technology, engineering, and mathematics (STEM) pathways by (1) providing opportunities for students to learn CT within the regular school day, in core science classrooms; and (2) by reframing coding as a tool for developing solutions to compelling real‐world problems. We present core pedagogical strategies employed in the CT+S instructional model and describe its implementation into two 10‐lesson instructional units for middle‐school science classrooms. In the first unit, students create computational models of a coral reef ecosystem. In the second unit, students write code to create, analyze, and interpret data visualizations using a large air quality dataset from the United States Environmental Protection Agency to understand, communicate, and evaluate solutions for air quality concerns. In our investigation of the model's implementation through these two units, we found that participating students demonstrated statistically significant advancements in CT, competency beliefs for computation in STEM, and value assigned to computation in STEM. We also examine evidence for how the CT+S model's core pedagogical strategies may be contributing to observed outcomes. We discuss the implications of these findings and propose a testable theory of action for the model that can serve future researchers, evaluators, educators, and instructional designers. 

Computational Thinking for Science: Positioning coding as a tool for doing science

Abstract This article focuses on uncertainty— ways in which scientists recognize and analyze limits in their studies and conclusions. We distinguish uncertainty from (un)trustworthiness—ways in which scientific reports can be affected by conscious deception or unconscious bias. Scientific journal articles typically include analyses and quantifications of uncertainty in both quantitative forms (e.g., error bars, ranges of predictions, statistical tests) and qualitative forms (e.g., alternate hypotheses, limitations of studies, questions for future research). These analyses of uncertainty are often incorporated into reports from scientific organizations and responsible scientific journalism. We argue that a critical goal of science education should be to help students understand how science may be employed as an uncertain and limited, yet still useful tool for informing decisions about socioscientific problems. When members of the public are insufficiently prepared to understand analyses and quantifications of uncertainty, the consequences are manifest in public skepticism about science and inadequately informed decision-making about socioscientific issues. We describe current design work in science education that includes a worthwhile emphasis on helping students to recognize and leverage uncertainty in their own data and models. Additional important work can enable students to develop proficiency in seeking out and understanding analyses of continuing uncertainty in media accounts of scientific conclusions and predictions. 

/pdf/untangling-trustworthiness-and-uncertainty-in-science-39ngiq37.pdf

Untangling Trustworthiness and Uncertainty in Science

Using Computational Thinking and Modeling to Build Water and Watershed Literacy

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsKristin L. GunckelKristin Gunckel (kgunckel@email.arizona.edu) is a professor of science education at the University of Arizona, Tucson, AZ; Beth A. Covitt is Head of Science Education Research and Evaluation at the spectrUM Discovery Area at the University of Montana-Missoula, Missoula, MT; Garrett Love is an instructor of engineering at the North Carolina School of Science and Mathematics, Durham, NC; Judith A. Cooper-Wagoner is a lecturer in science education at the University of Arizona, Tucson, AZ, and Dan Moreno is a graduate research assistant at the University of Arizona, Tucson, AZ.Beth A. CovittKristin Gunckel (kgunckel@email.arizona.edu) is a professor of science education at the University of Arizona, Tucson, AZ; Beth A. Covitt is Head of Science Education Research and Evaluation at the spectrUM Discovery Area at the University of Montana-Missoula, Missoula, MT; Garrett Love is an instructor of engineering at the North Carolina School of Science and Mathematics, Durham, NC; Judith A. Cooper-Wagoner is a lecturer in science education at the University of Arizona, Tucson, AZ, and Dan Moreno is a graduate research assistant at the University of Arizona, Tucson, AZ.Garrett LoveKristin Gunckel (kgunckel@email.arizona.edu) is a professor of science education at the University of Arizona, Tucson, AZ; Beth A. Covitt is Head of Science Education Research and Evaluation at the spectrUM Discovery Area at the University of Montana-Missoula, Missoula, MT; Garrett Love is an instructor of engineering at the North Carolina School of Science and Mathematics, Durham, NC; Judith A. Cooper-Wagoner is a lecturer in science education at the University of Arizona, Tucson, AZ, and Dan Moreno is a graduate research assistant at the University of Arizona, Tucson, AZ.Judith A. Cooper-WagonerKristin Gunckel (kgunckel@email.arizona.edu) is a professor of science education at the University of Arizona, Tucson, AZ; Beth A. Covitt is Head of Science Education Research and Evaluation at the spectrUM Discovery Area at the University of Montana-Missoula, Missoula, MT; Garrett Love is an instructor of engineering at the North Carolina School of Science and Mathematics, Durham, NC; Judith A. Cooper-Wagoner is a lecturer in science education at the University of Arizona, Tucson, AZ, and Dan Moreno is a graduate research assistant at the University of Arizona, Tucson, AZ.Dan MorenoKristin Gunckel (kgunckel@email.arizona.edu) is a professor of science education at the University of Arizona, Tucson, AZ; Beth A. Covitt is Head of Science Education Research and Evaluation at the spectrUM Discovery Area at the University of Montana-Missoula, Missoula, MT; Garrett Love is an instructor of engineering at the North Carolina School of Science and Mathematics, Durham, NC; Judith A. Cooper-Wagoner is a lecturer in science education at the University of Arizona, Tucson, AZ, and Dan Moreno is a graduate research assistant at the University of Arizona, Tucson, AZ. 

Unplugged to Plugged In

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