Mid-America Science Museum will implement a professional development program for its education staff and those from member museums of the Arkansas Discovery Network. Museum staffers will participate in a series of three day-long workshops on robotics, app development, and microprocessors. Workshop follow-up will be in the form of strategically scheduled internet-based meetings, an online community, and various methods of evaluation. The program will provide up-to-date professional development and training in newer technologies for educators in the museum and from across Arkansas. Training will encourage these educators to develop their own activities to increase audience engagement and use modern technology to create powerful professional development opportunities for teachers. The project will advance the museum's strategic goal of being a leader in informal science education and creating professional development opportunities for museum educators across the region.
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
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 Computational Thinking in Ecosystems (CT-E) project is funded by the STEM+Computing Partnership (STEM+C) program, which seeks to advance new approaches to, and evidence-based understanding of, the integration of computing in STEM teaching and learning. The project is a collaboration between the New York Hall of Science (NYSCI), Columbia University's Center for International Earth Science Information Network, and Design I/O. It will address the need for improved data, modeling and computational literacy in young people through development and testing of a portable, computer-based simulation of interactions that occur within ecosystems and between coupled natural and human systems; computational thinking skills are required to advance farther in the simulation. On a tablet computer at NYSCI, each participant will receive a set of virtual "cards" that require them to enter a computer command, routine or algorithm to control the behavior of animals within a simulated ecosystem. As participants explore the animals' simulated habitat, they will learn increasingly more complex strategies needed for the animal's survival, will use similar computational ideas and skills that ecologists use to model complex, dynamic ecological systems, and will respond to the effects of the ecosystem changes that they and other participants elicit through interaction with the simulated environment. Research on this approach to understanding interactions among species within biological systems through integration of computing has potential to advance knowledge. Researchers will study how simulations that are similar to popular collectable card game formats can improve computational thinking and better prepare STEM learners to take an interest in, and advance knowledge in, the field of environmental science as their academic and career aspirations evolve. The project will also design and develop a practical approach to programing complex models, and develop skills in communities of young people to exercise agency in learning about modeling and acting within complex systems; deepening learning in young people about how to work toward sustainable solutions, solve complex engineering problems and be better prepared to address the challenges of a complex, global society.
Computational Thinking in the Ecosystems (CT-E) will use a design-based study to prototype and test this novel, tablet-based collectable card game-like intervention to develop innovative practices in middle school science. Through this approach, some of the most significant challenges to teaching practice in the Next Generation Science Standards will be addressed, through infusing computational thinking into life science learning. CT-E will develop a tablet-based simulation representing six dynamic, interconnected ecosystems in which students control the behaviors of creatures to intervene in habitats to accomplish goals and respond to changes in the health of their habitat and the ecosystems of which they are a part. Behaviors of creatures in the simulation are controlled through the virtual collectable "cards", with each representing a computational process (such as sequences, loops, variables, conditionals and events). Gameplay involves individual players choosing a creature and habitat, formulating strategies and programming that creature with tactics in that habitat (such as finding food, digging in the ground, diverting water, or removing or planting vegetation) to navigate that habitat and survive. Habitats chosen by the participant are part of particular kinds of biomes (such as desert, rain forest, marshlands and plains) that have their own characteristic flora, fauna, and climate. Because the environments represent complex dynamic interconnected environmental models, participants are challenged to explore how these models work, and test hypotheses about how the environment will respond to their creature's interventions; but also to the creatures of other players, since multiple participants can collaborate or compete similar to commercially available collectable card games (e.g., Magic and Yu-Go-Oh!). NYSCI will conduct participatory design based research to determine impacts on structured and unstructured learning settings and whether it overcomes barriers to learning complex environmental science.
Reconceptualizing STEM + Computing Literacy is funded by the STEM+Computing Partnership (STEM+C) program, which seeks to advance multidisciplinary integration of computing and computational thinking in K-12 science, technology, engineering, and mathematics (STEM) teaching and learning through applied research and development across one or more domains, and broadening participation in computing and computing-related fields. The project will study the integration of computational thinking as part of a new and more contemporary perspective of STEM literacy, and will design, develop, and beta-test a prototype literacy assessment tool that will measure computational thinking literacy along with measures of literacy in other STEM content areas. The tool will be available to the general public as a self-measurement application (App) that can be used by individuals to test their own literacy, and by teachers, schools, and informal educators and organizations to assess literacy development in their students and in their STEM education programs. This transdisciplinary research project will begin the process of creating an innovative approach and tool for measuring literacy that will expand the definition of literacy to include computational skills along with science reasoning. Literacy is an important concept and measurement that has traditionally been used to assess an individual's knowledge of science. This project will explore a broader literacy perspective that incorporates learning derived from out of school and one that incorporates computational skills and thinking as part of a more contemporary perspective of STEM literacy. A prototype web-based App allowing individuals and education organizations to assess literacy levels, and ways to enhance literacy, will be developed and studied. The methodology will be developed using discussions and knowledge from over 60 experts across computing, education, science, social science, and other STEM fields using a Delphi method to engage in reconceptualization of literacy. The hypothesis is that this new STEM+C literacy framework should be structured along four interacting but semi-independent domains: 1) general STEM+C knowledge; 2) self-defined areas of STEM+C knowledge and expertise; 3) attitudes and beliefs related to STEM+C; and 4) the skills and competencies necessary to participate in STEM+C related pursuits and discussions, including measures of modes of STEM+C thinking. Each of these four domains is likely to include numerous sub-domains and associated descriptors, which collectively describe the different aspects of being a STEM+C literate citizen. The application will be designed to provide feedback to individuals on their knowledge, attitudes and skills compared with those of others and suggest ways to enhance and improve their skills and understanding through an embedded feedback mechanism. This project creates public benefit by providing individuals and organizations with a responsive real-time understanding measuring STEM+C literacy, deepening the dialogue about the value of public engagement in science, engineering, technology, math and computing and revealing the dynamic factors that inform STEM+C literacy.
In this literature review, we seek to understand in what ways aspects of computer science education and making and makerspaces may support the ambitious vision for science education put forth in A Framework for K-12 Science as carried forward in the Next Generation Science Standards. Specifically, we examine how computer science and making and makerspace approaches may inform a project-based learning approach for supporting three-dimensional science learning at the elementary level. We reviewed the methods and findings of both recently published articles by influential scholars in computer
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TEAM MEMBERS:
Samuel SeveranceSusan CodereEmily MillerDeborah Peek-BrownJoseph Krajcik
As the maker movement is increasingly adopted into K-12 schools, students are developing new competences in exploration and fabrication technologies. This study assesses learning with these technologies in K-12 makerspaces and FabLabs.
Our study describes the iterative process of developing an assessment instrument for this new technological literacy, the Exploration and Fabrication Technologies Instrument, and presents findings from implementations at five schools in three countries. Our index is generalizable and psychometrically sound, and permits comparison between student confidence
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TEAM MEMBERS:
Paulo BliksteinZaza KabayadondoAndrew P. MartinDeborah A. Fields
Women continue to be underrepresented in computer science professions. In 2015, while 57% of professional occupations in the U.S. were held by women, only 25% of computing occupations were held by women. Furthermore, the share of computer science degrees going to women is smaller than any STEM field, even though technology careers are the most promising in terms of salaries and future growth. Research suggests that issues contributing to this lack of computer science participation begin early and involve complex social and environmental factors, including girls' perception that they do not belong in computer science classes or careers. Computer science instruction often alienates girls with irrelevant curriculum; non-collaborative pedagogies; a lack of opportunities to take risks or make mistakes; and a heavy reliance on lecture instead of hands-on, project-based learning. Computer science experiences that employ research-based gender equitable best practices, particularly role modeling, can help diminish the gender gap in participation. In response to this challenge, Twin Cities PBS (TPT), the National Girls Collaborative (NGC) and Code.org will lead Code: SciGirls! Media for Engaging Girls in Computing Pathways, a three-year project designed to engage 8-13 year-old girls in coding through transmedia programming which inspires and prepares them for future computer science studies and career paths. The project includes five new PBS SciGirls episodes featuring girls and female coding professionals using coding to solve real problems; a new interactive PBSKids.org game that allows children to develop coding skills; nationwide outreach programming, including professional development for informal educators and female coding professionals to facilitate activities for girls and families in diverse STEM learning environments; a research study that will advance understanding of how the transmedia components build girls' motivation to pursue additional coding experiences; and a third-party summative evaluation.
Code: SciGirls! will foster greater awareness of and engagement in computer science studies and career paths for girls. The PBS SciGirls episodes will feature girls and female computer science professionals using coding to solve real-world challenges. The project's transmedia component will leverage the television content into the online space in which much of 21st century learning takes place. The new interactive PBSKids.org game will use a narrative framework to help children develop coding skills. Drawing on narrative transportation theory and character identification theory, TPT will commission two exploratory knowledge-building studies to investigate: To what extent and how do the narrative formats of the Code: SciGirls! online media affect girls' interest, beliefs, and behavioral intent towards coding and code-related careers? The studies aim to advance understanding of how media builds girls' motivation to pursue computer science experiences, a skill set critical to building tomorrow's workforce. The project team will also raise educators' awareness about the importance of gender equitable computer science instruction, and empower them with best practices to welcome, prepare and retain girls in coding. The Code: SciGirls! Activity Guide will provide educators with a relevant resource for engaging aspiring computer scientists. The new media and guide will also reside on PBSLearningMedia.org, reaching 1.2 million teachers, and will be shared with thousands of educators across the SciGirls CONNECT and National Girls Collaborative networks. The new episodes are anticipated to reach 92% of U.S. TV households via PBS, and the game at PBSKids.org will introduce millions of children to coding. The summative evaluation will examine the reach and impact of the episodes, game and new activities. PIs will share research findings and project resources at national conferences and will submit to relevant publications. This project 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, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.
Becoming computationally literate is increasingly crucial to everyday life and to expanding workforce capacity. Research suggests that computational literacy--knowing what, when, how, and why to use the ideas of computer science, in combination with the capacity to view problems and potential solutions through the lens of computational structures and procedures--can be supported through digital game play. This project aims to develop a social and creative exhibit game that foregrounds aspects of computer science, specifically artificial intelligence (AI) and computer programming, in ways that enable youth to explore, construct, and share computational complex systems content with one another and other museum visitors. To play the game, pairs of youth visitors will use code cards to program the behavior of AI animals in a virtual forest. As they do so, youth will engage with computational literacy practices, such as basic computer programming, describing their computational ideas, and doing computational problem solving with their friends. Their activity will be projected on a large screen as a strategy for enabling youth to test, rehearse, and communicate their computational ideas and to also interest other visitors into computational problem solving.
Using multi-perspective and iterative design-based research, university learning scientists, museum practitioners, and game developers will pursue research questions around how science museums can better engage youth who are traditionally underrepresented in computer science in complex computational practices. Data sources will include interactive-log data, observations of visitor interactions with the game, visitor interviews, and visitor surveys. A multimodal and mixed methods approach that searches for convergences between qualitative analysis, quantitative analysis, and learning analytics will be used to generate research findings. Changes in computational literacy will be assessed by evaluating what problems visitors choose to solve with programming, how they frame those problems, and their selections from among possible solutions, what they program, how they program, and how they describe programming ideas. The results of this project will include: 1) a social, interactive gameplay experience that supports the development of computational literacy; 2) design principles for game-based exhibits that facilitate development of computational literacy; and 3) new knowledge of variations in design and gameplay across diverse gameplay users, including those from underrepresented groups in computer science. It is anticipated that 1,000 museum youth visitors will directly participate in the study.
This project 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, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.
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TEAM MEMBERS:
Matthew BerlandLeilah LyonsMatthew Cannady
As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches and resources for use in a variety of settings. This study will capitalize on the increased availability and affordability of immersive interactive technologies, such as Augmented Reality devices and virtual characters, to investigate their potential for benefitting STEM learning in informal museum contexts. This project will combine these technologies to create an Augmented Reality experience that will allow middle-school youth and their families to meet and assist a virtual crew on a historic ship at the Independence Seaport Museum in Philadelphia. The players in this game-like experience will encounter technologies from the turn of the 20th century, including steam power, electricity, and wireless communication. Crew members and technologies will be brought to life aboard the USS Olympia, the largest and fastest ship in the US Navy launched in 1892. The historic context will be positioned in relation to current day technologies in ways that will enable a change in interest towards technology and engineering in middle school-age youth. This will result in a testbed for the feasibility of facilitating short-term science, technology, engineering and mathematics (STEM) identity change with interactive immersive technologies. A successful feasibility demonstration, as well as the insights into design, could open up novel ways of fostering STEM interest and identity in informal learning contexts and of demonstrating the impact of this approach. The potential benefit to society will rest in the expected results on the basic science regarding immersive interactive technologies in informal learning contexts as well as in demonstrating the feasibility of the integrated approach to assessment.
This project will use a living lab methodology to evaluate interactive immersive technologies in terms of their support for STEM identity change in middle-school age youth. The two-year design-based research will iteratively develop and improve the measurement instrument for the argument that identity change is a fundamental to learning. A combination of Augmented Reality and intelligent virtual agents will be used to create an interactive experience--a virtual living lab--in an informal museum learning exhibit that enables change interests towards technology and engineering and provides short-term assessment tools. In collaboration with the Independence Seaport Museum in Philadelphia, the testbed for the approach will be an experience that brings to life the technologies of the early 20th century aboard a historic ship. Through the application of Participatory Action Research techniques, intelligent virtual agents interacting with youth and families will customize STEM information relating to the ship's mission and performance. Topics explored will make connections with current day technologies and scientific understanding. Mixed-methods will be used to analyze interactions, interview and survey data, will form the basis for assessing the impact on youth's STEM interests. The elicitation method specifically includes assessment metrics that are relevant to the concept of learning as identity change. This assessment, through immersive interactive technologies, will target the priority areas of engagement in STEM as well as the measurement of outcomes.
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TEAM MEMBERS:
Stefan RankAyana AllenGlen MuschioAroutis FosterKapil Dandekar
The cyberlearning community in the United States brings computer scientists and learning scientists together to design and study innovative learning technologies. The Cyberlearning Community Report: The State of Cyberlearning and the Future of Learning With Technology highlights examples of the exciting work our community is engaged in as we integrate the latest innovations in learning science and computer science into new research designs and methods. This work is also driving the need for new learning sciences in areas such as embodied cognition, identity, and affect, and requires advances
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