This award takes an innovative approach to an ongoing, pervasive, and persistent societal issue: women are still drastically underrepresented in computing careers. This project targets middle school-aged girls because it is a time when many of them lose interest and confidence in pursuing technical education and computing careers. This project will design, develop, and deploy a one-week experience focused on middle school girls that targets this issue with a novel combination of teaching techniques and technology. The project will use wearable computing devices to support girls' social interactions as they learn computing and solve technical challenges together. The goals of the project are to raise interest, perceived competence, and involvement in the computational ability of girls. Additionally, the project aims to increase a sense of computational community for girls that makes pursuing computational skills more relevant to their identities and lives, and that helps continued participation in computing. The project will deploy a one-week experience four times per year with a socioeconomically diverse range of campers. The project will also develop a 'program in a box' kit that can be broadly used by others wishing to deliver a similar experience for girls.
The planned research will determine if a one-week experience that uses social wearable construction in the context of live-action role play can use the mediating process of computational community formation to positively impact middle school girls' engagement with and interest in computation. Computational community is defined as girls engaging together in the process of learning computation, trading resources and knowledge, and supporting growth. Research participants will include 100 6th to 9th-grade girls. At least 75% of the participants will be either low income, first-generation college-bound, or underrepresented in higher education. Students will be recruited through the longstanding partnerships with title one schools in the Salinas Valley, the Educational Partnership Center, and in the Pajaro Valley Unified School district, where 82% of the students are Hispanic/Latinx, 42% are English Learners, and 73% are eligible for free or reduced lunch. The research questions are: 1) Does the proposed experience increase girls' self-reported competence, self-efficacy, and interest in computational skills and careers? and 2) Will the proposed experience lead to activity-based evidence of learning and integration of computational skills at the group social level? The project will use a mixed-methods, design-based research approach which is an iterative design process to rapidly collect and analyze data, and regularly discuss the implications for practice with the design team. Data will be collected using observations, interviews, focus groups, surveys, and staff logs. Quantitative data will be analyzed using frequencies, means, and measures of dispersion will be applied to survey data from both time points. Pearson correlation coefficients will be used to describe the bivariate relationship between continuous factors. ANOVAs will assess whether there are significant differences in continuous measures across groups. Qualitative data will be analyzed using a constant comparison method.
This Innovations in Development award is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
This RAPID was submitted in response to the NSF Dear Colleague letter related to the COVID-19 pandemic. This award is made by the AISL program in the Division of Research on Learning, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act. COVID-19 presents a national threat to the health of children and families, presenting serious implications for the mental and physical health of children. Child development scientists have already warned of increasing stress levels among the U.S. child population, especially those in low-income families of color. In addition, Latino children are disproportionately impoverished, and benefit from culturally relevant information. Parents and caregivers need to be armed with effective science-based strategies to improve child prospects during this global crisis. Harnessing well-established partnership (including with local TV news partners and parent-serving organizations) strengthens the potential for broad impacts on the health and well-being of children and families during the COVID-19 pandemic. As the pandemic persists, widely disseminating accurate research-based strategies to support parents and families, with a focus on low-income Latino parents, is crucial to meeting the needs of the nation's most vulnerable during this global crisis. The award addresses this urgent need by producing research-based news videos on child development for distribution on broadcast television stations that reach low income Latino parents. The videos will communicate research-based recommendations regarding COVID-19 in ways that are relatable to Latino parents and lead to positive parenting during this pandemic. A "how to" video will also be produced showing parents how to implement some of the practices. Project partners include Abriendo Puertas, the largest U.S. parenting program serving low-income Latinos, and Ivanhoe Broadcasting.
Research questions include: 1) What information do parents need (and potentially what misinformation they are being exposed to)? 2) What are they sharing? 3) How does this vary geographically? 4) Can researchers detect differences in public engagement in geographic areas where TV stations air news videos as compared to areas that don't? This project will use data and communication science research strategies (e.g. natural language processing from online sites where parents are asking questions and sharing information) to inform the content of the videos and lead to the adoption of featured behaviors. Data from web searches, public Facebook pages, and Twitter posts will be used to gain a window into parents' main questions and concerns including information regarding hygiene, how to talk about the pandemic without frightening their children, or determining veracity of what they hear and see related to the pandemic.
This organic approach can detect concerns that parents may be unlikely to ask doctors or discuss in focus groups. Methodologically, the researchers will accomplish this by natural language analysis of the topics that parents raise; the words and phrases they use to talk about specific content; and any references to external sources of information. Where possible, the researchers will segment this analysis by geography to see if there are geographical differences in information needs and discourse. A research brief will share new knowledge gained with the field on how to respond to national emergencies, such as the COVID-19 pandemic, using local TV news and reinforcement of messages across contexts. The findings from this award will provide a knowledge base that can be utilized to better inform responses to national emergencies in the future. By broadly disseminating these findings through a research brief, the project?s innovative research will advance the field of communication science.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
AHA! Island is a new project that uses animation, live-action videos, and hands-on activities to support joint engagement of children and caregivers around computational thinking (CT) concepts and practices. Education Development Center (EDC), WGBH’s research partner for the project, conducted an impact study with 108 English-speaking families (4- to 5-year-old children and their families) to test the promise of this CT learning intervention.
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TEAM MEMBERS:
Marisa WolskyHeather LavigneJessica AndrewsAshley Lewis-PresserLeslie CuellarRegan VidiksisCamille Ferguson
Increasing demand for curricula and programming that supports computational thinking in K-2 settings motivates our research team to investigate how computational thinking can be understood, observed, and supported for this age group. This study has two phases: 1) developing definitions of computational thinking competencies, 2) identifying educational apps that can potentially promote computational thinking. For the first phase, we reviewed literatures and models that identified, defined and/or described computational thinking competencies. Using the model and literature review, we then
We found that the learners seeking out resources to teach themselves to code were generally college educated women who were motived either by the desire to be able to read and understand the code written by hired developers or the desire to become developers themselves. The importance of a female-focused learning setting was mixed; while most women acknowledged a more comfortable atmosphere created by such a setting, very few cited that as a primary reason for joining the group.
All learner participants in this study persisted through the ten weeks of the Women’s Coaching and Learning
In 2016, ETR received a National Science Foundation grant to study, under Principal Investigator Louise Ann (“Lou Ann”) Lyon, PhD, a newly formed, real-world organization dedicated to helping women in the workforce learn to write computer code. This project formed a partnership between a research team with experience in computer science (CS) education and learning sciences research and a newly fashioned practitioner team focused on building a grassroots, informal, volunteer group created to help women help themselves and others learn to write computer code. This research-practitioner
This article reflects the results of the project “Open Access Statistics”, which was designed to collect standardized usage figures for scientific documents. The data gathered were primarily intended to provide impact values based on document usage for Open Access documents as these were excluded from databases used to provide citation based impact scores. The project also planned the implementation of more sophisticated procedures such as network analyses, but was confronted with complex legal requirements.
This commentary introduces a preliminary conceptual framework for approaching putative effects of scholarly online systems on collaboration inside and outside of academia. The first part outlines a typology of scholarly online systems (SOS), i.e., the triad of specialised portals, specialised information services and scholarly online networks which is developed on the basis of nine German examples. In its second part, the commentary argues that we know little about collaborative scholarly community building by means of SOS. The commentary closes with some remarks on further research questions
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
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
The rise of artificial intelligence has recently led to bots writing real news stories about sports, finance and politics. As yet, bots have not turned their attention to science, and some people still mistakenly think science is too complex for bots to write about. In fact, a small number of insiders are now applying AI algorithms to summarise scientific research papers and automatically turn them into simple press releases and news stories. Could the science beat be next in line for automation, potentially making many science reporters --- and even editors --- superfluous to science