Integrating science, technology, engineering, and mathematics (STEM) subjects in pre-college settings is seen as critical in providing opportunities for children to develop knowledge, skills, and interests in these subjects and the associated critical thinking skills. More recently computational thinking (CT) has been called out as an equally important topic to emphasize among pre-college students. The authors of this paper began an integrated STEM+CT project three years ago to explore integrating these subjects through a science center exhibit and a curriculum for 5-8 year old students. We
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Morgan HynesMonica CardellaTamara MooreSean BrophySenay PurzerKristina TankMuhsin MeneskeIbrahim YeterHoda Ehsan
Computational Thinking (CT) is an often overlooked, but important, aspect of engineering thinking. This connection can be seen in Wing’s definition of CT, which includes a combination of mathematical and engineering thinking required to solve problems. While previous studies have shown that children are capable of engaging in multiple CT competencies, research has yet to explore the role that parents play in promoting these competencies in their children. In this study, we are taking a unique approach by investigating the role that a homeschool mother played in her child’s engagement in CT
Informal learning environments such as science centers and museums are instrumental in the promotion of science, technology, engineering, and mathematics (STEM) education. These settings provide children with the chance to engage in self-directed activities that can create a of lifelong interest and persistence in STEM. On the other hand, the presence of parents in these settings allows children the opportunity to work together and engage in conversations that can boost understanding and enhance learning of STEM topics. To date, a considerable amount of research has focused on adult-child
Computational Thinking (CT) is a relatively new educational focus and a clear need for learners as a 21st century skill. This proposal tackles this challenging new area for young learners, an area greatly in need of research and learning materials. The Principal Investigators will develop and implement integrated STEM+C museum exhibits and integrate CT in their existing engineering design based PictureSTEM curriculum for K-2 students. They will also pilot assessments of the CT components of the PictureSTEM curriculum. This work will make a unique contribution to the available STEM+C learning materials and assessments. There are few such materials for the kindergarten to second grade (K-2) population they will work with. They will research the effects of the curriculum and the exhibits with a mixed methods approach. First, they will collect observational data and conduct case studies to discover the important elements of an integrated STEM+C experience in both the formal in-school setting with the curriculum and in the informal out-of-school setting with families interacting with the museum exhibits. This work will provide a novel way to understand the important question of how in- and out-of-school experiences contribute to the development of STEM and CT thinking and learning. Finally, they will collect data from all participants to discover the ways that their activities lead to increases in STEM+C knowledge and interest.
The Principal Investigators will build on an integrated STEM curriculum by integrating CT and develop integrated museum exhibits. They base both activities on engineering design implemented through challenge based programming activities. They will research and/or develop assessments of both STEM+C integrated thinking and CT. Their research strategy combines Design Based Research and quantitative assessment of the effectiveness of the materials for learning CT. In the first two years of their study, they will engage in iterations on the design of the curriculum and the exhibits based on observation and case-study data. There will be 16 cases that draw from each grade level and involve data collection for the case student in both schools and museums. They will also use this work to illuminate what integrated STEM+C thinking and learning looks like across formal and informal learning environments. Based in some part on what they discover in this first phase, they will conduct the quantitative assessments with all (or at least most) students participating in the study
This paper contributes a theoretical framework informed by historical, philosophical and ethnographic studies of science practice to argue that data should be considered to be actively produced, rather than passively collected. We further argue that traditional school science laboratory investigations misconstrue the nature of data and overly constrain student agency in their production. We use our “Data Production” framework to analyze activity of and interviews with high school students who created data using sensors and software in a ninth-grade integrated science class. To understand the
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
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
Grassroots women's learn-to-code groups are springing up in many places. This infographic a study of one such group, in which more-knowledgeable "coaches" lead novice "learners" in learning software programming on the Salesforce platform. This study found that women create such groups to have supportive, non-threatening environments that nuture their learning to build confidence before entering male-dominated software development communities.
Increasingly, scientists and their institutions are engaging with lay audiences via media. The emergence of social media has allowed scientists to engage with publics in novel ways. Social networking sites have fundamentally changed the modern media environment and, subsequently, media consumption habits. When asked where they primarily go to learn more about scientific issues, more than half of Americans point to the Internet. These online spaces offer many opportunities for scientists to play active roles in communicating and engaging directly with various publics. Additionally, the proposed research activities were inspired by a recent report by the National Academies of Sciences, Engineering, and Medicine that included a challenge to science communication researchers to determine better approaches for communicating science through social media platforms. Humor has been recommended as a method that scientists could use in communicating with publics; however, there is little empirical evidence that its use is effective. The researchers will explore the effectiveness of using humor for communicating about artificial intelligence, climate science and microbiomes.
The research questions are: How do lay audiences respond to messages about scientific issues on social media that use humor? What are scientists' views toward using humor in constructing social media messages? Can collaborations between science communication scholars and practitioners facilitate more effective practices? The research is grounded in the theory of planned behavior and framing as a theory of media effects. A public survey will collect and analyze data on Twitter messages with and without humor, the number of likes and re-tweets of each message, and their scientific content. Survey participants will be randomly assigned to one of twenty-four experimental conditions. The survey sample, matching recent U.S. Census Bureau data, will be obtained from opt-in panels provided by Qualtrics, an online market research company. The second component of the research will quantify the attitudes of scientists toward using humor to communicate with publics on social media. Data will be collected from a random sample of scientists and graduate students at R1 universities nationwide. Data will be analyzed using descriptive statistics and regression modeling.
The broader impacts of this project are twofold: findings from the research will be shared with science communication scholars and trainers advancing knowledge and practice; and an infographic (visual representation of findings) will be distributed to practitioners who participate in research-practice partnerships. It will provide a set of easily-referenced, evidence-based guidelines about the types of humor to which audiences respond positively on social media.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
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Sara YeoLeona Yi-Fan SuMichael Cacciatore
Research that seeks to understand classroom interactions often relies on video recordings of classrooms so that researchers can document and analyze what teachers and students are doing in the learning environment. When studies are large scale, this analysis is challenging in part because it is time-consuming to review and code large quantities of video. For example, hundreds of hours of videotaped interaction between students working in an after-school program for advancing computational thinking and engineering learning for Latino/a students. This project is exploring the use of computer-assisted methods for video analysis to support manual coding by researchers. The project is adapting procedures used for computer-aided diagnosis systems for medical systems. The computer-assisted process creates summaries that can then be used by researchers to identify critical events and to describe patterns of activities in the classroom such as students talking to each other or writing during a small group project. Creating the summaries requires analyzing video for facial recognition, motion, color and object identification. The project will investigate what parts of student participation and teaching can be analyzed using computer-assisted video analysis. This project is supported by NSF's EHR Core Research (ECR) program, the STEM+C program and the AISL program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. The project is funded by the STEM+Computing program, which seeks to address emerging challenges in computational STEM areas through the applied integration of computational thinking and computing activities within disciplinary STEM teaching and learning in early childhood education through high school (preK-12). As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program 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.
The video analysis systems will provide video summarizations for specific activities which will allow researchers to use these results to quantify student participation and document teaching practices that support student learning. This will support the analysis of large volumes of video data that are often time-consuming to analyze. The video analysis system will identify objects in the scene and then use measures of distances between objects and other tracking methods to code different activities (e.g., typing, talking, interaction between the student and a facilitator). The two groups of research questions are as follows. (1) How can human review of digital videos benefit from computer-assisted video analysis methods? Which aspects of video summarization (e.g., detected activities) can help reduce the time it takes to review the videos? Beyond audio analytics, what types of future research in video summarization can help reduce the time that it takes to review videos? (2) How can we quantify student participation using computer-assisted video analysis methods? What aspects of student participation can be accurately measures by computer-assisted video analysis methods? The video to be used for this study is drawn from a project focused on engineering and computational thinking learning for Latino/a students in an after-school setting. Hundreds of hours of video are available to be reviewed and analyzed to design and refine the system. The resulting coding will also help document patterns of engagement in the learning environment.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
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TEAM MEMBERS:
Marios PattichisSylvia Celedon-PattichisCarlos LopezLeiva
Robots and robotics excite and challenge youths and adults. Unfortunately, the cost of purchasing robots or building useful robots is prohibitive for many low resource individuals and groups. This project will relieve this expense and provide an opportunity for resource limited individuals to experience the thrilling aspects of robotics by building a computer game that simulates robotic action. This project uses co-robotics wherein the participating player programs an avatar to assist in a symbiotic manner to achieve the goals of the game and participant. The game will provide access to the ideas and concepts such as programing, computational thinking and role assumption. The overarching goals are (1) to engage low-resource learners in STEM education through robotics in out-of-school spaces, and (2) to update the field of robotics-base STEM education to integrate the co-robotics paradigm.
This project is designed to gain knowledge on how co-robotics can be used in the informal education sector to facilitate the integration of computational science with STEM topics and to expand the educational use of co-robotics. Because the concept of co-robotics is new, a designed-based research approach will be used to build theoretical knowledge and knowledge of effective interventions for helping participants learn programing and computational thinking. Data will be collected from several sources including surveys, self-reports, in game surveys, pre and post-tests. These data collection efforts will address the following areas: Technology reliability, Resolution of cognitive tension around co-play, Accelerate discovery and initial engagement, Foster role-taking and interdependence with co-robots, Investigate social learning, and Validate measures using item response theory analysis. The DBR study questions are:
1.What design principles support the development of P3Gs that can effectively attract initial engagement in a free-choice OST space that offers large numbers of competing options? 2.What design principles support a P3G gameplay loop that enables learning of complex skills, computational thinking and co-robotics norms, and building of individual and career interest over the course of repeated engagement?
3.What design principles support P3Gs in attaining a high rate of re-engagement within low-resource OST settings? 4.What kinds of positive impact can P3Gs have on their proximal and distal environment? In addition, the project will research these questions about design: 1.What technical and game design features are needed to accommodate technological interruption? 2.What design elements or principles mitigate competition for cognitive resources between real-time play and understanding the co-robotic's behavior in relation to the code the player wrote for it? 3.What design elements are effective at getting learners in OST settings to notice and start playing the game? 4.What designs are effective at encouraging learners to engage with challenging content, particularly the transition from manual play to co-play? 5.What design elements help players develop a stake in the role the game offers? 6.What social behaviors emerge organically around a P3G prototype that is not designed to evoke specific social interactions?
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
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.