Christian D. Schunn - US grants
Affiliations: | University of Pittsburgh, Pittsburgh, PA, United States |
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The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Christian D. Schunn is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2002 — 2006 | Schunn, Christian Raghavan, Kalyani |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Pittsburgh The Learning Research and Development Center at the University of Pittsburgh will conduct a longitudinal study in which researchers will create a three-year sequence of model-centered instruction in the context of the Model-Assisted Reasoning in Science (MARS). MARS current topics will be extended from sixth through eighth grades. The project seeks to: (1) understand how working external models support content and process learning, (2) develop an evaluation model to tap strengths and weaknesses of different kinds of external models, and (3) identify pedagogical strategies that elicit and support model-assisted reasoning. Student content knowledge and process skills will be measured through different test formats that include paper-and-pencil (TIMSS, NAEP, and Test of Scientific Reasoning items), written tests, class work, and classroom computer exercises. Student motivation will be measured at the beginning of each year. A small sample of students will be interviewed and given some transfer tasks twice a year. Interviews will focus on two aspects: properties of the different model types and student's metacognitive understanding of the function of models in science. Information on classroom implementation will be collected by direct observation, videotapes, and interviews with teachers. Results of the study are expected to help extend theories of model-based reasoning and its applicability in classrooms. |
0.915 |
2005 — 2009 | Lovell, Michael Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Pittsburgh This award provides funding for a 3 year continuing award to support a Research Experiences for Teachers (RET) Site program at the University of Pittsburgh entitled, "Bringing Innovative Design into Urban High Schools on a Sustainable Basis: University of Pittsburgh Design Team RET Site," under the direction of Dr. Michael R. Lovell. The mission of this RET site program is to implement several of the University of Pittsburgh's innovative design research activities at the high school level in an effort to foster creativity and promote interest in science, technology, and math (STEM) subjects, particularly for underrepresent pre-college students. Two well recognized units of the University of Pittsburgh are joining together to attain this mission-the Swanson Center for Product Innovation (SCPI) and the Learning Research and Development Center (LRDC). To gain an additional real-world industry perspective, Westinghouse Electric Corporation (WEC) will also significantly contribute to the propsed research program by providing leadership, projects and financial support for the RET activities. |
0.915 |
2006 — 2007 | Schunn, Christian | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Workshop On the Scientific Basis of Individual and Team Innovation and Discovery @ University of Pittsburgh How do scientists and engineers discover and innovate? The creative process is often portrayed as one shrouded in mystery, and the domains of science and engineering are no exception. Imagine the stereotype of the eccentric professor, for instance, staring intently at a blackboard full of equations, who then suddenly sees the answer that had been hidden for months or even years. Or imagine a team of engineers going out for lunch to take a break from an impasse they have hit, and in the middle of casual conversation they come upon a novel approach to the problem, one that elegantly gets around the impasse. These scenarios depict the births of discoveries and innovations that, with nurturing and perseverance, can grow to have long-lasting impacts on the fields of science and engineering, and the world at large. |
0.915 |
2006 — 2007 | Schunn, Christian | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Conference: Support For Computational Cognitive Modeling At Cogsci 2006 @ University of Pittsburgh Across the sciences, computational modeling is one the most widely-used methods for linking different levels of analysis, to show how processes at one level can interact to produce emergent system behavior at another level. Within the cognitive science, computational modeling has been used to link neural, mental, and behavioral levels of analysis, for instance. Computational modeling has also served as a common ground for drawing links across the various disciplines that comprise the cognitive sciences, such as cognitive neuroscience, linguistics, computer science, psychology, anthropology, philosophy, and education. Computational modeling has clearly become a valuable and oftentimes needed skill in the cognitive sciences, yet there are barriers to acquiring and practicing this skill for students and junior investigators entering the field. To encourage the practice of computational modeling, the National Science Foundation will contribute to prizes and tutorials for computational modeling submissions to the 2006 Meeting of the Cognitive Science Society. The prizes and tutorials will be aimed at getting students interested and involved in computational modeling, and to provide some initial training that might spur them to seek further training through their institutions and other resources. |
0.915 |
2006 — 2008 | Lovell, Michael Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Towards a Science of Innovative Design @ University of Pittsburgh Engineering design is not a purely mental process. Successful design engineers move from ideas to completed designs using artifacts and tools. Over the past several decades, the number of tools and artifacts available to engineers has become virtually limitless. These tools include drawing programs, quantitative modeling software, sketch paper, CAD programs, and prototyping facilities. Despite their importance for supporting creativity and innovation, little is known about the role of tools in supporting the cognitive processes of innovative design. |
0.915 |
2007 — 2008 | Schunn, Christian | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cogsci2007 Conference: Workshop On Computational Cognitive Modeling @ University of Pittsburgh Across the sciences, computational modeling is one the most widely-used methods for linking different levels of analysis, to show how processes at one level can interact to produce emergent system behavior at another level. Within the cognitive science, computational modeling has been used to link neural, mental, and behavioral levels of analysis, for instance. Computational modeling has also served as a common ground for drawing links across the various disciplines that comprise the cognitive sciences, such as cognitive neuroscience, linguistics, computer science, psychology, anthropology, philosophy, and education. Computational modeling has clearly become a valuable and oftentimes needed skill in the cognitive sciences, yet there are barriers to acquiring and practicing this skill for students and junior investigators entering the field. To encourage the practice of computational modeling, the National Science Foundation will support student training in computational modeling at the 2007 Meeting of the Cognitive Science Society. Support is aimed at getting students interested and involved in computational modeling, and providing tutorials that might spur them to seek further training opportunities through their institutions and other resources. |
0.915 |
2008 — 2010 | Lovell, Michael Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Mod: Design Tools to Cognitive Processes to Innovation @ University of Pittsburgh The U.S. is facing serious challenges in the fields of science and technology and our future engineers must use innovation to generate new products, create employment opportunities, and strengthen the national economy. Furthermore, the existing connections between cognitive science and engineering are nationally quite small. This study provides a much needed diverse set of investigators to bridge knowledge across these areas. |
0.915 |
2008 — 2011 | Schunn, Christian | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Mod: Integrating Social and Cognitive Elements of Discovery and Innovation @ University of Pittsburgh Innovation and discovery involve individuals working successfully together in teams. It is critical for the Science of Science and Innovation Policy to understand how the cognition of individuals, the direct source of novel ideas and critical decision making, is impacted by social teamwork variables. Prior research has typically studied social teamwork variables in isolation or individual cognition variables in isolation. To know how to intervene to increase engineering and scientific output, the relationships between the two must be known, or else we might improve one at the cost of hurting the other, which likely would have no net improvement in final scientific or engineering productivity. The current project examines a very large quantity of video data collected from a recent highly successful case of science and engineering, the Mars Exploration Rover, which both wildly exceeded engineering requirements for the mission and produced many important scientific discoveries. Yet, not all days of the mission were equally successful. From this video record, the project traces the path from the structure of different subgroups (such as having formal roles and diversity of knowledge in the subgroups) to the occurrence of different social processes (such as task conflict, breadth of participation, communication norms, and shared mental models) to the occurrence of different cognitive processes (such as analogy, information search, and evaluation) and finally to outcomes (such as new methods for rover control and new hypotheses regarding the nature of Mars). |
0.915 |
2008 — 2011 | Lovell, Michael Schunn, Christian Landis, Amy [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Pittsburgh This award provides funding for a 3 year continuing award to support a Research Experiences for Teachers (RET) in Engineering Site program at the University of Pittsburgh entitled, "Connecting Research and Teaching Through Product Realization: The Pittsburgh Quality of Life RET Site," under the direction of Dr. Michael Lovell. |
0.915 |
2009 — 2010 | Schunn, Christian | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Pittsburgh Understanding how organizations promote innovation is a key element of advancing the science of innovation, and hence the science of innovation policy. Data on innovation inputs, innovation processes, and innovation outputs are increasingly being captured and stored electronically. A number of fundamental bottlenecks to using these data to advance social science research exist due to unsolved issues of privacy, data integration, and data quality. The core scientific challenge is how to make such real-world, large-scale data available to researchers to nurture innovation and perform valid experimentation, while maintaining data privacy. Fortunately, computer scientists have been developing a variety of techniques and building new tools that manage large data sets in ways that can potentially help in supporting and measuring innovation activities. |
0.915 |
2009 — 2013 | Schunn, Christian | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Pittsburgh The research objective of this award is to improve understanding and capabilities in concept generation through design by analogy methods. The proposed approach, a collaboration between the disciplines of cognitive psychology, engineering design and computer science, is to provide new tools for design based on a representation that associates functional and geometric information, combining a linguistic search for functional similarity with a multi-level search for geometric similarity to automatically identify and present analogies to the designer. The initial application for the Verrocchio Project is the design of prosthetic and orthotic devices for persons with disabilities, a domain that is ripe for innovation. The initial search space is the USPTO utility patent repository. Deliverables will include: (1) means to more effectively generalize design problems through functional descriptions; (2) the ability to search for analogical solutions with alternative functional representations; (3) ways to search for geometric similarities across a set of functional analogies; and (4) the ability to produce a tractable set of analogies for use by the designer. |
0.915 |
2010 — 2013 | Stein, Mary Kay Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Strategies: the Robot Algebra Project @ University of Pittsburgh The Robot Algebra Project creates three scalable, middle school level units for use in informal settings. The units are designed around fundamental robot movement concepts but emphasize proportional reasoning - a big idea in mathematics. There are over 12,000 FIRST Lego League teams across the U.S. that purport to use robots as a motivator to engage students in STEM. However, most of the time the students use guess and check procedures thwarting the opportunity to learn STEM content. The units being developed build upon model eliciting activities, project-based learning and mathematics education to specifically improve student understanding of a few key mathematics concepts. The programming of robots is scaffolded so that students concentrate on the mathematics. Rather than only doing hands-on activities, the students also produce toolkits for other students to engage in similar experiments. Paper- based word problems are developed to bridge the mathematics learned in the context of robotics to generalized mathematical problem-solving strategies. Professional development is provided both face-to-face and through webinars to early adopters who are also trained to provide professional development to others. Materials to supplement the professional development are produced to support teachers and informal educators understanding of the rationale, the agenda, the mathematics and the perspectives that underlie the student materials as well as to also support them in anticipating student responses to the tasks. The materials can be updated online. |
0.915 |
2010 — 2015 | Stein, Mary Kay Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Modeling Engineered Levers For the 21st Century Teaching of Stem @ University of Pittsburgh Research in biology has become increasingly mathematical, but high school courses in biology use little mathematics. To address this concern, this project will develop three replacement units for biology and refine them through classroom testing. The units will be models of STEM integration by using the important concepts of proportional reasoning and algebraic thinking and engineering re-design to address big ideas in science while also promoting the learning of 21st century skills. The materials build on existing work on the use of model eliciting activities and focus science and technology instruction on high-stakes weaknesses in mathematics and science. They address the scaling issue as part of the core design work by developing small units of curriculum that can be applied by early adopters in each context. The materials will undergo many rounds of testing and revision in the early design process with at least ten teachers each time. The materials will be educative for teachers, and the teacher materials and professional development methods will work at scale and distance. |
0.915 |
2011 — 2017 | Litman, Diane (co-PI) [⬀] Ashley, Kevin [⬀] Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Dip: Teaching Writing and Argumentation With Ai-Supported Diagramming and Peer Review @ University of Pittsburgh The PIs are investigating the design of intelligent tutoring systems (ITSs) that are aimed at learning in unstructured domains. Such systems are not able to do as much automatically as ITSs working in traditionally narrow and well-structured domains, but rather they need to share responsibilities for scaffolding learning with a teacher and/or peers. In the work proposed, the three PIs, who share expertise in automated natural language understanding, intelligent tutoring systems, machine learning, argumentation (especially in law), complex problem solving, and engineering education, are integrating intelligent tutoring, data mining, machine learning, and language processing to design a socio-technical system (people and machines working together) that helps undergraduates and law students write better argumentative essays. The work of helping learners derive an argument is shared by the computer and peers, as is the work of helping peer reviewers review the writing of others and the work of learners to turn their argument diagrams into well-written documents. Research questions address the roles computers might take on in promoting writing and the technology that enables that, how to distribute scaffolding between an intelligent machine and human agents, how to promote better writing (especially the relationship between diagramming and writing), and how to promote learning through peer review of the writing of others. |
0.915 |
2012 — 2016 | Pearlman, Jonathan [⬀] Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Connecting Research and Teaching Through Product Innovation: Quality of Life Technology Ret Site @ University of Pittsburgh This award provides funding for a 3 year continuing award to support a Research Experiences for Teachers (RET) in Engineering and Computer Science Site program at the University of Pittsburgh entitled, "Connecting Research and Teaching Through Product Innovation: Quality of Life Technology RET Site under the direction of Dr. Jonathan L. Pearlman. |
0.915 |
2013 — 2017 | Schunn, Christian Russell, Jennifer |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Pittsburgh This is a project to improve understanding of practices critical to the design of curricular materials for implementation in a broad range of educational contexts. Three organizations - TERC, the University of California-Berkeley's Lawrence Hall of Science, and the University of Pittsburgh's Learning Research and Development Center - will collaborate to explore and codify practices that enhance the success of efforts to design K-12 science curriculum materials for large-scale implementation. Investigators from these three organizations will conduct and synthesize results from a series of retrospective and live-design practice, broad and 'deep dive' studies, with the goal of articulating a conceptual model of educational design for large-scale use. Of particular concern are the processes and strategies designers employ to address key challenges to producing curricular materials capable of having meaningful impacts on large numbers of learners (e.g., to achieve deep understanding and rich performance, to connect to and leverage diverse social and cultural experiences, and to facilitate implementation in diverse and resource-limited settings). These issues will be explored from a variety of perspectives, including: interviews with designers and document reviews to identify structural project characteristics that appear to be empirically associated with scaling success; retrospective case studies to identify salient features and lessons learned from more and less successful large-scale design initiatives for science education; and deep dives (involving participant-observation, interviews, focus group discussions, and document analysis) into sustained design practices over an extended period to explore how design teams address key design challenges while developing educational materials for large-scale use. |
0.915 |
2014 — 2016 | Chan, Chu Sern Joel Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Pittsburgh Innovation fundamentally begins with a good idea. But where do good ideas come from? Much research suggests that innovative breakthroughs are often inspired by past experience: things and ideas that one has interacted with in the world. However, the same experiences that can inspire innovation can also can constrain or harm innovation through focus on previously unsuccessful solutions. This project tests principles for guiding interactions with sources of inspiration to maximize their benefits and minimize their pitfalls. In particular, it focuses on the role of conceptual distance of sources. The following questions are the focus of this work: 1) Are good ideas built mainly on sources that are closely related to the problem (e.g., building on existing recycling efforts to address the problem of people throwing away electronics), or are they most often inspired by sources that are from distantly related domains (e.g., being inspired by how burrs cling to a dog's fur when designing Velcro)? 2) When considering multiple sources, should one try to ensure that the sources are similar to each other (i.e., deeply exploring one direction), or should one consider diverse sources? |
0.915 |
2014 — 2017 | Stein, Mary Kay Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Changing Culture in Robotics Classroom @ University of Pittsburgh Computational and algorithmic thinking are new basic skills for the 21st century. Unfortunately few K-12 schools in the United States offer significant courses that address learning these skills. However many schools do offer robotics courses. These courses can incorporate computational thinking instruction but frequently do not. This research project aims to address this problem by developing a comprehensive set of resources designed to address teacher preparation, course content, and access to resources. This project builds upon a ten year collaboration between Carnegie Mellon's Robotics Academy and the University of Pittsburgh's Learning Research and Development Center that studied how teachers implement robotics education in their classrooms and developed curricula that led to significant learning gains. This project will address the following three questions: |
0.915 |
2014 — 2017 | Schunn, Christian Crowley, Kevin |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Cer: Building a Theory of Badges For Computer Science Education @ University of Pittsburgh The University of Pittsburgh and Carnegie Mellon University will test and refine a Theory of Badges applied to Computer Science Education. Given that badges have recently attracted a great deal of attention as both motivator and assessment marker, the goal of the project is to create a foundation of principles that will guide the design of future Computer Science badging initiatives. The team will experimentally manipulate the use of badges within an ongoing Computer Science education development project that leverages highly popular robotics competitions as the distribution channel. They will monitor and adapt the form and content of assessments and badge representations in computer science content modules to try to achieve the best possible outcomes for student participants (learner persistence, computer science content learning, and computer science career interest) as predicted by the current iteration of their badge theory. Researchers will explicitly track and publish data for underrepresented minorities, women, and economically disadvantaged students to see whether badges are particularly effective or encounter obstacles specific to these populations. |
0.915 |
2014 — 2017 | Schunn, Christian Crowley, Kevin |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Studying the Malleability and Impact of Science Learning Activation @ University of Pittsburgh This project, conducted by the University of Pittsburgh and the University of California, Berkeley, seeks to discover what makes middle school students engaged in science, technology, engineering, and mathematics (STEM). The researchers have developed a concept known as science learning activation, including dispositions, practices, and knowledge leading to successful STEM learning and engagement. The project is intended to develop and validate a method of measuring science learning activation. |
0.915 |
2014 — 2017 | Litman, Diane (co-PI) [⬀] Schunn, Christian Godley, Amanda |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
An Intelligent Ecosystem For Science Writing Instruction @ University of Pittsburgh The ability to express scientific ideas in both written and oral form is an important 21st century skill. Teachers, employers, and college faculty lament the inability of many high school graduates to write clearly. This deficit in writing is due in part because teachers do not have the time to provide appropriate, timely feedback to students on their writing. This project would help teachers help students achieve these skills through automating an effective feedback process, in ways that are customized to particular disciplines and local classroom needs, particularly in high needs districts. The project will contribute to knowledge about how students learn to write and how computer assisted systems can support this learning. |
0.915 |
2015 — 2020 | Grabowski, Joseph (co-PI) [⬀] Kaufmann, Nancy (co-PI) [⬀] Singh, Chandralekha (co-PI) [⬀] Nokes-Malach, Timothy [⬀] Schunn, Christian |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Build, Understand, & Tune Interventions That Cumulate to Real Impact @ University of Pittsburgh The University of Pittsburgh has received an NSF Improving Undergraduate STEM Education: Education and Human Resources Design and Development tier award to bring together a highly interdisciplinary team to study a suite of instructional, cognitive-skill, and social/motivational interventions that have been demonstrated to produce large improvements in learning in the context of introductory STEM courses. This research is significant because it will allow us to understand which interventions produce long-term positive outcomes, whether these interventions combine negatively or synergistically within and across courses, and the types of situations or groups of students for which they are most effective. |
0.915 |