YR Media (formerly Youth Radio) engages young people in digital media production that combines journalism, design, data, and coding. With support from the National Science Foundation (NSF), YR Media collaborated with the Massachusetts Institute of Technology’s App Inventor to launch WAVES — A STEM-Powered Youth News Network for the Nation. This three-year initiative expanded YR Media’s model of informal STEM education through the launch of a national platform that utilizes STEM-powered tools to create and distribute news stories, mobile apps, and digital interactives.
Rockman et al, an
This document contains a description and summaritve evaluation information for the TV Weathercasters and Climate Education award, including impacts of the program on television weathercasters and on their public audiences. The project team documented substantial increases in both the science-based views and climate reporting practices of TV weathercasters. They also found that viewers appreciated climate reporting by local TV weathercasters, feeling that it provided them with a helpful local perspective on a global problem.
Seeking to use narrative as a vehicle for getting more young people interested in STEM, the National Science Foundation supported a WETA/PBS NewsHour initiative to adapt their Student Reporting Labs (SRL) curriculum to feature a focus on STEM content in 2015. NewsHour selected Knology to run a four-year evaluation of the project from 2015 to 2019. Building on earlier research about student-led science journalism (e.g. Polman & Hope, 2014; Nicholas, 2017), this project suggests that having students develop and produce narratives about complex STEM topics may make these topics far less
The independent evaluation firm, Knight Williams, Inc., developed a two-part post-program survey to gather information about the Year 1 SciGirls CONNECT2 outreach programs conducted by 14 partner organizations. The evaluation aimed for one educator from each organization to complete Part 1 of the survey, which consisted of program reporting questions. In all, one educator from 13 partner organizations completed Part 1, for a response rate of 93%. Part 2 of the survey asked for program reflections, with a focus on perceived program goals, impacts, highlights, and challenges. Given the
The independent evaluation firm Knight Williams, Inc. conducted a formative evaluation during Year 2 of the SciGirls CONNECT2 program in order to gather information about the partner educators’ use of, reflections on, and recommendations relating to the draft updated SciGirls Strategies. The evaluation aimed for two educators from each of 14 partner organizations – specifically the program leader and one educator who was familiar with the original SciGirls Seven – to provide reflections on their use of the draft SciGirls Strategies in their programs through an online survey and follow-up
The independent evaluation firm, Knight Williams, Inc., administered an online survey and conducted follow-up interviews with educators from 14 SciGirls CONNECT2 partner organizations to gather information about their use of, reflections on, and recommendations relating to the SciGirls Seven strategies. The evaluation aimed for two educators from each partner organization – specifically the program leader and one educator who was familiar with the SciGirls Seven – to share reflections on the strategies after they completed their Year 1 programs. In all, 24 educators from 13 partners completed
This report presents findings of the Latina SciGirls mixed methods study, investigating the experiences of young Latinas participating in informal STEM programs across the U.S. that utilized the SciGirls educational model (including the SciGirls Seven strategies) and augmented with materials and practices intended to better serve Hispanic girls. The project was led by Twin Cities Public Television with funding from the National Science Foundation as an AISL Innovations in Development project. The STEM-related identity framework and research model used to guide this investigation is presented
Knight Williams, Inc. completed a summative evaluation report that addresses: (i) the reach and breadth of the Latina SciGirls broadcast program and online components compared to project expectations; (ii) the impact of the Family Fiesta events that incorporated use of SciGirls videos, in-person role models, and hands-on activities as experienced by the girls, family members, and role models that participated in the events; and (iii) the partners’ Latina SciGirls programs and how they used and reflected on the value of the SciGirls resources.
How can creators of STEM learning media reach underserved parents and children, and support the kinds of playful STEM interactions that are foundational for future STEM learning?
This research report summarizes findings from a pilot study of Cyberchase: Mobile Adventures in STEM, a program that uses mobile text messaging and short videos to encourage hands-on family learning among low-income Latino families.
In the study, 95 mostly Latino families received weekly text messages with video clips from the popular children's series Cyberchase, and fun activities to do with their
Participants in this study reported a variety of resources used in the past to learn to code in Apex, including online tutorials, one-day classes sponsored by Salesforce, and meet-up groups focused on learning. They reported various difficulties in learning through these resources, including what they viewed as the gendered nature of classes where the men already seemed to know how to code—which set a fast pace for the class, difficulty in knowing “where to start” in their learning, and a lack of time to practice learning due to work and family responsibilities. The Coaching and Learning Group
This INSPIRE award is partially funded by the Cyber-Human Systems Program in the Division of Information and Intelligent Systems in the Directorate for Computer Science and Engineering, the Gravitational Physics Program in the Division of Physics in the Directorate for Mathematical and Physical Sciences, and the Office of Integrative Activities.
This innovative project will develop a citizen science system to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO), the most complicated experiment ever undertaken in gravitational physics. Before the end of this decade it will open up the window of gravitational wave observations on the Universe. However, the high detector sensitivity needed for astrophysical discoveries makes aLIGO very susceptible to noncosmic artifacts and noise that must be identified and separated from cosmic signals. Teaching computers to identify and morphologically classify these artifacts in detector data is exceedingly difficult. Human eyesight is a proven tool for classification, but the aLIGO data streams from approximately 30,000 sensors and monitors easily overwhelm a single human. This research will address these problems by coupling human classification with a machine learning model that learns from the citizen scientists and also guides how information is provided to participants. A novel feature of this system will be its reliance on volunteers to discover new glitch classes, not just use existing ones. The project includes research on the human-centered computing aspects of this sociocomputational system, and thus can inspire future citizen science projects that do not merely exploit the labor of volunteers but engage them as partners in scientific discovery. Therefore, the project will have substantial educational benefits for the volunteers, who will gain a good understanding on how science works, and will be a part of the excitement of opening up a new window on the universe.
This is an innovative, interdisciplinary collaboration between the existing LIGO, at the time it is being technically enhanced, and Zooniverse, which has fielded a workable crowdsourcing model, currently involving over a million people on 30 projects. The work will help aLIGO to quickly identify noise and artifacts in the science data stream, separating out legitimate astrophysical events, and allowing those events to be distributed to other observatories for more detailed source identification and study. This project will also build and evaluate an interface between machine learning and human learning that will itself be an advance on current methods. It can be depicted as a loop: (1) By sifting through enormous amounts of aLIGO data, the citizen scientists will produce a robust "gold standard" glitch dataset that can be used to seed and train machine learning algorithms that will aid in the identification task. (2) The machine learning protocols that select and classify glitch events will be developed to maximize the potential of the citizen scientists by organizing and passing the data to them in more effective ways. The project will experiment with the task design and workflow organization (leveraging previous Zooniverse experience) to build a system that takes advantage of the distinctive strengths of the machines (ability to process large amounts of data systematically) and the humans (ability to identify patterns and spot discrepancies), and then using the model to enable high quality aLIGO detector characterization and gravitational wave searches
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TEAM MEMBERS:
Vassiliki KalogeraAggelos KatsaggelosKevin CrowstonLaura TrouilleJoshua SmithShane LarsonLaura Whyte
This handout was prepared for the Climate Change Showcase at the 2019 ASTC Conference in Toronto, Ontario. It highlights resources available on InformalScience.org related to the topic of climate change.