This paper attempts to reframe popular notions of “failure” as recently celebrated in the Maker Movement, Silicon Valley, and beyond. Building on Vossoughi et al.’s 2013 FabLearn publication describing how a focus on iterations/drafts can serve as an equity-oriented pedagogical move in afterschool tinkering contexts, we explore what it means for afterschool youth and educators to persist through unexpected challenges when using an iterative design process in their tinkering projects. More specifically, this paper describes: 1) how young women in a program geared toward increasing equitable
Maker Education scholarship is accumulating increasingly complex understandings of the kinds of learning associated with maker practices along with principles and pedagogies that support such learning. However, even as large investments are being made to spread maker education, there is little understanding of how organizations that are intended targets of such investments learn to develop new maker related educational programs. Using the framework of Expansive Learning, focusing on organizational learning processes resulting in new and unfolding forms of activity, this paper begins to fill
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 Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. This project brings together two approaches to help K-12 students learn programming and computer science: open-ended learning environments, and computer-based learning analytics, to help create a setting where youth can get help and scaffolding tailored to what they know about programming without having to take tests or participate in rigid textbook exercises for the system to know what they know.
The project proposes to use techniques from educational data mining and learning analytics to process student data in the Alice programming environment. Building on the assessment design model of Evidence-Centered Design, student log data will be used to construct a model of individual students' computational thinking practices, aligned with emerging standards including NGSS and research on assessment of computational thinking. Initially, the system will be developed based on an existing corpus of pair-programming log data from approximately 600 students, triangulating with manually-coded performance assessments of programming through game design exercises. In the second phase of the work, curricula and professional development will be created to allow the system to be tested with underrepresented girls at Stanford's CS summer workshops and with students from diverse high schools implementing the Exploring Computer Science curriculum. Direct observation and interviews will be used to improve the model. Research will address how learners enact computational thinking practices in building computational artifacts, what patters of behavior serve as evidence of learning CT practices, and how to better design constructionist programming environments so that personalized learner scaffolding can be provided. By aligning with a popular programming environment (Alice) and a widely-used computer science curriculum (Exploring Computer Science), the project can have broad impact on computer science education; software developed will be released under a BSD-style license so others can build on it.
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TEAM MEMBERS:
Shuchi GroverMarie BienkowskiJohn Stamper
C-RISE will create a replicable, customizable model for supporting citizen engagement with scientific data and reasoning to increase community resiliency under conditions of sea level rise and storm surge. Working with NOAA partners, we will design, pilot, and deliver interactive digital learning experiences that use the best available NOAA data and tools to engage participants in the interdependence of humans and the environment, the cycles of observation and experiment that advance science knowledge, and predicted changes for sea level and storm frequency. These scientific concepts and principles will be brought to human scale through real-world planning challenges developed with our city and government partners in Portland and South Portland, Maine. Over the course of the project, thousands of citizens from nearby neighborhoods and middle school students from across Maine’s sixteen counties, will engage with scientific data and forecasts specific to Portland Harbor—Maine’s largest seaport and the second largest oil port on the east coast. Interactive learning experiences for both audiences will be delivered through GMRI’s Cohen Center for Interactive Learning—a state-of-the-art exhibit space—in the context of facilitated conversations designed to emphasize how scientific reasoning is an essential tool for addressing real and pressing community and environmental issues. The learning experiences will also be available through a public web portal, giving all area residents access to the data and forecasts. The C-RISE web portal will be available to other coastal communities with guidance for loading locally relevant NOAA data into the learning experience. An accompanying guide will support community leaders and educators to embed the interactive learning experiences effectively into community conversations around resiliency. This project is aligned with NOAA’s Education Strategic Plan 2015-2035 by forwarding environmental literacy and using emerging technologies.
Keystone Connect Network is a proposed regional broadband network whose purpose is to increase educational opportunities and generate business growth. The backbone of this plan is the Pennsylvania Research and Education Network's (PennREN), a next generation high-speed internet network, managed by KINBER, which educational institutions can use to train their students and create new learning opportunities; and business can create new products and connect with their customers.
This project takes an ethnographic and design-based approach to understanding how and what people learn from participation in makerspaces and explores the features of those environments that can be leveraged to better promote learning. Makerspaces are physical locations where people (often families) get together to make things. Some participants learn substantial amounts of STEM content and practices as they design, build, and iteratively refine working devices. Others, however, simply take a trial and error approach. Research explores the affordances are of these spaces for promoting learning and how to integrate technology into these spaces so that they are transformed from being makerspaces where learning happens, but inconsistently, into environments where learning is a consistent outcome of participation. One aim is to learn how to effectively design such spaces so that participants are encouraged and helped to become intentional, reflective makers rather than simply tinkerers. Research will also advance what is known about effective studio teaching and learning and advance understanding of how to support youth to help them become competent, creative, and reflective producers with technology(s). The project builds on the Studio Thinking Framework and what is known about development of meta-representational competence. The foundations of these frameworks are in Lave and Wengers communities of practice and Rogoff's, Stevens et al.'s, and Jenkins et al.'s further work on participatory cultures for social networks that revolve around production. A sociocultural approach is taken that seeks to understand the relationships between space, participants, and technologies as participants set and work toward achieving goals. Engaging more of our young population in scientific and technological thinking and learning and broadening participation in the STEM workplace are national imperatives. One way to address these imperatives is to engage the passions of young people, helping them recognize the roles STEM content and practices play in achieving their own personal goals. Maker spaces are neighborhood spaces that are arising in many urban areas that allow and promote tinkering, designing, and construction using real materials, sometimes quite sophisticated ones. Participating in designing and successfully building working devices in such spaces can promote STEM learning, confidence and competence in one's ability to solve problems, and positive attitudes towards engineering, science, and math (among other things). The goal in this project is to learn how to design these spaces and integrate learning technologies so that learning happens more consistently (along with tinkering and making) and especially so that they are accessible and inviting to those who might not normally participate in these spaces. The work of this project is happening in an urban setting and with at-risk children, and a special effort is being made to accommodate making and learning with peers. As with Computer Clubhouses, maker spaces hold potential for their participants to identify what is interesting to them at the same time their participation gives them the opportunity to express themselves, learn STEM content, and put it to use.
The Maker Movement is a community of hobbyists, tinkerers, engineers, hackers, and artists who creatively design and build projects for both playful and useful ends. There is growing interest among educators in bringing making into K-12 education to enhance opportunities to engage in the practices of engineering, specifically, and STEM more broadly. This article describes three elements of the Maker Movement, and associated research needs, necessary to understand its promise for education: 1) digital tools, including rapid prototyping tools and low-cost microcontroller platforms, that
Although computer science drives innovations that directly affect our everyday lives, few K–12 students have access to engaging and rigorous computer science learning. This article describes an effort to democratize access to computer science education through a program based on inquiry, culturally relevant curriculum, and equity-oriented pedagogy.
Afterschool continues to be promoted as a complementary setting to school for strengthening science, technology, engineering, and math (STEM) education (for example, Krishnamurthi, Bevan, Rinehart, & Coulon, 2013). This is a reasonable idea: 10.2 million children and youth in the U.S. participate in structured afterschool programs (Afterschool Alliance, 2014), and the flexibility of afterschool settings allows for innovative approaches to STEM exploration and engagement.
In partnership with Future Makers and several Maryland public libraries, the Maryland State Department of Education’s Division of Library Development and Services (DLDS) will teach children ages 4 to 7 the basic principles of programming through the use of Primo, an open-source robotics platform. With Primo, children use blocks to create algorithms that guide a robot through a maze. This will establish a foundation for learning more advanced programming skills later on, set early learners on the path to fluency in computer science, and establish a stronger mindset in computational thinking through play and experimentation. As Primo does not rely on a computer screen, the program will be replicable in a variety of environments; the curriculum will also be inclusive to young children with varying degrees of ability.
Georgetown County Library will improve the digital-age critical workforce skills of local young people through STEM-related digital activities. Classes relating to online STEM resources, digital video production, and app development will result in increased skills and interpersonal abilities, as well as an appreciation for the public library as a dynamic and informative place. By working with a number of community organizations, the library seeks to reach a local youth community that has historically experienced high rates of poverty and low rates of high school completion, and build on previous efforts to provide job fairs, skills training, and other initiatives.