Why are conclusions important in science

They are offered to all designers of citizen science projects, with the understanding—discussed throughout—that designers include a wide and representative range of stakeholders and that effective design extends well into implementation.

These overarching recommendations for enabling learning from citizen science are supplemented by a set of evidence-based suggestions that can be used by designers again, broadly construed to advance learning in specific citizen science projects.

These suggestions, or guidelines, are developed in detail in Chapter 6. In thinking about learning in citizen science, our committee was confronted early and often with the reality that citizen science is embedded in a larger set of cultural practices and that these practices can be less than equitable.

Assumptions about who is eligible and prepared to participate in science activities, what kind of knowledge counts as science, and even who is entitled to ask or answer scientific questions are influenced, not always positively, by attitudes and practices in society at large.

To put it another way, science, especially citizen science, is a sociocultural activity. Along with the positive learning opportunities that come with social and cultural interaction, comes the possibility of inheriting and propagating biases that inhibit learning. And these biases do not just inhibit learning for members of the groups that are targeted by the biases; by limiting the breadth of people and ideas, they inhibit learning for everyone.

In order to ensure that participation in citizen science does not unquestioningly accept biases and inadvertently propagate existing and historical inequities that undermine learning, the committee positioned its first recommendation around understanding issues of power and intentionally designing to promote equity.

The history of design and sociocultural theories of learning both make it clear that if citizen science stakeholders do not explicitly question implicit biases and inequitable distributions of power, and work to minimize their impact, they are likely to design proj-. Most often, the narrow range consists of members of dominant social groups, defined above. Conversely, explicitly considering inequity, finding ways to minimize barriers for all learners, and designing to welcome and respect members and ideas from nondominant groups can result in more diverse, equitable participation, which improves project outcomes for all stakeholders.

It also offers insight into how science as a whole can move toward more equitable outcomes and broader participation. In order to engage in this work, the committee recommends that designers, researchers, participants, and other stakeholders in citizen science examine existing inequities that can impede participation in all facets of citizen science, and design pathways around those inequities.

This work entails welcoming diverse ideas, methods, and epistemologies, particularly from communities whose contributions have been neglected or minimized, in the design and implementation of citizen science projects. In examining existing citizen science projects, the committee found a number of projects that could take better advantage of the state-of-the art understanding about science learning. Often, these projects were designed and led by scientists with deep expertise in the discipline of the project, but less experience in education, educational design, or education research.

Conversely, projects that involved education researchers, educators, and people with expertise in education presented more evidence of learning. By the same token, these kinds of partnerships can also help advance research about learning from citizen science. The committee cannot underscore the next point enough: Success in learning outcomes through citizen science is enhanced by intentionally designing for learning.

Design theory makes it clear that strong collaborations among multiple stakeholders helps to broaden participation and support learning. Strong collaborations approach citizen science design as a partnership where all stakeholders are active participants with valuable insights and contributions. In practice, this looks like engaging with potential stakeholders early and often in the process of designing a citizen science project or adapting an existing citizen science project to promote learning.

In those discussions, project leads should make a concerted effort to talk with individuals from a diverse range of communities to learn about their participation—what it might look like, what might get in the way, and what might produce more value for them to participate. If there are difficulties—for example, a community with limited access to the project—exploring how to overcome those barriers is preferable to not continuing to work with that community.

A leadership team that includes scientists and potential participants can facilitate these conversations. From these discussions, it can be helpful to build a prototype, and use that prototype to anchor subsequent discussions.

Strong collaborations grow from these discussions and the iterative work afterward, and they are aided by being explicit about the collaboration and developing a common and clear understanding around roles, decision making, data collections and sharing, and ownership of intellectual property.

As an emerging field, citizen science has opportunities to advance in itself, contribute to what we know about how people learn science,. The next several recommendations explore how to maximize that potential; they are recommendations for building the field of citizen science.

The committee was also asked to lay out a research agenda that can fill gaps in the current understanding of how citizen science can support science learning and enhance science education, and those recommendations are outlined below. Existing research can begin to point stakeholders toward understanding the mechanisms at work when attempting to design citizen science to support science learning. Given the somewhat nascent nature of the field of citizen science as its own research domain, however, more research on the long-term strategies for how to support science learning is necessary in order to clarify and develop evidence-based practices and understand common elements and variations across a variety of sociocultural and practical contexts.

The committee wishes to point out that design-based research may be especially fruitful here: Not only will future research inform the design of citizen science projects but also design-based research in citizen science could also offer significant contributions to developing and refining theories about learning in citizen science.

In particular, design-based research is well suited to characterize the challenges and opportunities presented by the range of contexts in which citizen science learning takes place. More rigorous research, more documentation of effective practice, and more attention to equity will grow the foundation of practice that can be used to advance learning outcomes in citizen science. Research is essential to continued advancement in citizen science, and formal, peer-reviewed research remains a gold standard for understanding how learning happens in citizen science and leveraging citizen science to advance our understanding of how people learn in many contexts.

This report is a starting point for future analyses that go into more depth on key parts of science learning or consider new results made available after this report. There are three important factors to consider. First, citizen science extends beyond academia, and this means that evidence for successful practices that advance learning can be found outside of published peer-reviewed journals. In the process of preparing this report, the committee learned from conversations with a variety of stakeholders, blogs, and other online communications about citizen science, posters and informal presentations,.

In disseminating strategies that are useful for supporting learning, the citizen science research community should continue to learn from a wide variety of communication formats and not confine itself to the peer-reviewed literature. Second, research should include attention to practice and link theory to application.

The committee heard from practitioners and researchers alike about the challenges of translating emerging research on learning to actual practice in citizen science. Citizen science, as a nascent field, does not have codified divisions between educational researchers and practitioners. For instance, practitioners and researchers involved in citizen science attend the same conference and are members of the same professional society. This interaction between research and practice is unique, and the committee sees it as an opportunity to investigate how research-to-practice can work well.

More importantly, we see the interplay of researchers and practitioners as one facet of productive collaborative design and urge the citizen science community to continue to welcome and respect contributions from both theory and practice.

An experiment usually begins with a hypothesis — a proposed outcome or explanation for an observation. To test whether the hypothesis was right, researchers usually will conduct a series of tests, collecting data along the way.

But in science, making sense of those data can be challenging. And not all scientists will read the same meaning out of the same group of numbers. They might hypothesize that fertilizer A will produce taller plants than fertilizer B. After applying the different fertilizers to various groups of plants, the data may show that on average, the plants treated with fertilizer A indeed were taller. But this does not necessarily mean that fertilizer A was responsible for the height difference.

In science, making — and believing — such conclusions will depend on how the data stand up to a type of math known as statistics. And they start right with the original hypothesis. Scientists will expect one treatment — here, a fertilizer — to perform differently than another. But to enter the testing without bias, scientists also need to concede that their proposed explanation might be wrong.

So each hypothesis should therefore also have a corresponding null hypothesis — an understanding that there may be no change. In this experiment, a null hypothesis would hold out the prospect that the plants might respond identically to both fertilizers. But for the findings of these tests to be reliable, the experiment needs to test the effects on enough plants. How many? So before starting the tests, the researchers must calculate the minimum number of plants they must test.

And to do that, they must anticipate the chance that they could make either of two main types of errors when testing their null hypothesis. The first, called a Type I error, is a so-called false positive. A Type II error would conclude the opposite. This so-called false negative would conclude a fertilizer had no effect on plant height when in fact it did. Scientists in many fields, such as biology and chemistry, generally believe that a false-positive error is the worst type to make. But because no experiment ever works perfectly, scientists tend to accept there is some chance an error actually will occur.

If the test data indicated the chance this had happened was no higher than 5 percent written as 0. Include key facts from your background research to help explain your results as needed. State whether your results support or contradict your hypothesis. If appropriate, state the relationship between the independent and dependent variable. Summarize and evaluate your experimental procedure, making comments about its success and effectiveness.

Overview Your conclusions will summarize whether or not your science fair project results support or contradict your original hypothesis. If Your Results Show that Your Hypothesis is False If the results of your science experiment did not support your hypothesis, don't change or manipulate your results to fit your original hypothesis, simply explain why things did not go as expected.

Sample Here are sample conclusions. Explore Our Science Videos. Determine if the data is reproducible and if the sampling is done randomly. Let's try making a valid conclusion together. Read the description of the investigation and then analyze the graph.

Practice: Drawing Conclusions Now, it is your turn to practice drawing conclusions. Print Share. Conclusions and Scientific Explanations Copy and paste the link code above. Related Items Resources No Resources. Videos No videos.

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