Our epistemic cognition informs what scientific claims we choose to endorse over others, how we come to know in science, and our assumptions about the construction of scientific knowledge. The topic of climate change provides context for how we come to know about our surrounding environment. The development of climate literacy in young learners has received heightened attention over the last decade. What learners choose to believe about the topic of climate change presents an epistemic challenge for science educators as they help students navigate through a sea of information that often contains competing claims. The study described here examines how climate literacy and epistemic cognition interact in a group of 8th grade students in the Midwestern United States. Findings from Rasch analysis of survey responses and coding of student interviews suggests a positive relationship between learners' climate literacy and epistemic cognition, with participants tending to exhibit quasireflective judgment when justifying their beliefs about the causes and effects of climate change, how scientists come to know about Earth's climate, and the level of certainty that researchers have about changes to our climate system. Implications for the development of
The field of data science and associated diversity of domain specific applications is rapidly growing. Simultaneously, given the growing volume of data, science is becoming more interdisciplinary and compute-intensive. Yet data science and domain science have traditionally been taught separately. Given the cross-discipline demand for data science skills, it is important to consider what is taught, how it is taught, and who has access to these skills. This paper presents a model for teaching earth and environmental data science (EDS) that fuses data science skills with science domain knowledge, understanding of scientific data types and structures, and the communication and collaboration skills needed to work in interdisciplinary team environments. Our model also empowers a diversity of students through supporting student-directed learning using hybrid in-person and online classrooms, project-based learning to make content relatable and build student confidence, and open education resources to make curriculum more broadly accessible. Our model is founded upon evaluation and assessment which allows us to identify student pain points and iteratively improve as needed. This model could be applied to existing data science and science courses in an effort to support the workforce demand for skills at the intersection of science and data science.
Researchers in Earth and environmental science can extract incredible value from high resolution remote sensing data, but these data can be hard to use. Pain free use requires skills from remote sensing and the data sciences that are seldom taught together. In practice, many researchers teach themselves how to use high resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. Here we outline ten “rules” with examples from Earth and environmental science to help applied researchers work more effectively with high resolution data.
Researchers in Earth and environmental science can extract incredible value from highresolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data.
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