1997 — 2002 |
Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Career: Multivariate Visualization of Importance-Varying Data @ University of Mississippi
This project will investigate techniques for the display of importance-varying data in both two-and three-dimensional data domains. The approach to this problem will emphasize consideration of a) the perceptual characteristics of human viewers, b) dynamically manipulable display environments to increase the number of variables that can be clearly displayed, and c) experimental validation of the techniques developed. Portions of the project will be conducted in collaboration with the researchers in application fields such as meteorological monitoring, medical imaging, and public health decision-making. Specific project objectives include construction of a survey of existing importance visualization techniques, development of new techniques to represent importance-varying data, comparison and validation of techniques identified or developed, construction of a dynamic environment for the exploration of importance-varying data and the development and dissemination of perceptual principles for effective multivariate display.
|
1 |
2000 — 2005 |
Ebert, David Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Itr: Volume Illustration: Non-Photorealistic Rendering of Volume Models @ University of Maryland Baltimore County
This project studies the process of visualizing information at every point in space through volume rendering. Traditionally, volume rendering has employed one of two approaches. The first attempts a physically accurate simulation of a process such as X-rays passing through tissue or light passing through a fog, producing the most realistic views of volume data (at least for data with an appropriate physical meaning). The second approach is only loosely based on the physical behavior of light, using instead an arbitrary appearance of each value in space and an accumulation process through space to create a wider range of appearances for the volume in the visualization. This project proposes a new approach to volume rendering: the augmentation of a physics-based rendering process with non-photorealistic rendering (NPR) techniques to enhance the expressiveness of the visualization. NPR draws inspiration from such fields as art and technical illustration to develop automatic methods to synthesize images with an illustrated look from geometric surface models. The new approach, called volume illustration, combines the familiarity of a physics-based illumination model with the ability to enhance important features using non-photorealistic rendering techniques.
Technically, the project faces several challenges. In surface-based NPR, the surfaces (features) are well defined, whereas with volumes, volumetric feature areas are often amorphous regions that must be determined through analysis of local volumetric properties. Once these volumetric feature volumes are identified, user selected parametric properties can be used to enhance and illustrate them. Volume illustration provides a flexible unified framework for enhancing structural perception of volume models through the amplification of features, the addition of illumination effects, and the application of procedural textures. Volume illustration will work on both presampled and procedurally defined volume models, enabling a range of image styles from practical technical illustrations to more abstract painterly effects. The project will develop a collection of volume illustration techniques, including novel volume illustration techniques and techniques that adapt and extend NPR techniques to volume objects.
|
1 |
2001 — 2008 |
Hart, John (co-PI) [⬀] Ebert, David [⬀] Rheingans, Penny Marcum, David (co-PI) [⬀] Gaither, Kelly |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Itr/Ap+Im: Procedural Representation and Visualization Enabling Personalized Computational Fluid Dynamics
Computer power has increased dramatically over the past decade and has allowed computational fluid dynamics (CFD) researchers to more accurately simulate many types of complex flow. These simulations have enabled great leaps forward in the design and safety of ships, airplanes, automobiles, and other vehicles. However, this new power has also yielded terabytes of data, and CFD researchers now face a very difficult task in trying to find, extract, and analyze important flow features (e.g., time varying vortices, shock waves) buried within these monstrous datasets. Unlike the explosive growth in computer power, visualization tools for very large datasets have evolved modestly and cannot yet help with these tasks significantly. In particular, since detailed visualization of such large datasets is impractical, CFD researchers must work at a very cumbersome, low level to dice their datasets into workable pieces.
CFD researchers desperately need new techniques that simplify and automate the iterative process of finding the appropriate portion of their data set. They need a system that will allow the user to articulate appropriate types of features of interest, provide a compact representation of those features, and effectively visualize the feature information locally. The system will have to overcome the challenges of loading a sufficient portion of the data set over a network connection into a desktop machine, mapping the entire data set to a visual representation, and rendering the results at interactive rates.
This project will attack these CFD visualization problems by developing techniques for creating and using a procedural abstraction for a dataset. The major research objectives are to: 1. Detect features (e.g. shocks) in complex flows using topological operators. 2. Characterize the data relative to these features using a procedural representation consisting of implicit models and free-form deformations. 3. Adapt the procedural representation to the appropriate level of detail using multi-resolution techniques. 4. Encapsulate domain-specific knowledge as metadata to explore these extremely large datasets. 5. Visualize the data directly from the procedural representation. 6. Verify the accuracy of the procedural representation by tracking approximation error. 7. Apply these techniques to the large-scale computational flow simulation problems currently studied at Stanford and Mississippi State University. The resulting system will allow CFD researchers to work more effectively by interactively exploring their data to pinpoint the features of interest. Moreover, the results of this project will provide solutions not only for CFD researchers, but also for a wide variety of other visualization challenges and applications. The project's main goal is to develop techniques that allow visualization exploration, feature detection, extraction, and analysis at a higher, more effective level through the use of procedural data abstraction and representation.
|
0.961 |
2005 — 2010 |
Rheingans, Penny Desjardins, Marie [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Interactive Visual Methods For Partitioning Multidimensional Spatial Data @ University of Maryland Baltimore County
The goal of this research project is to develop innovative tools for interactive visual exploration of spatial multivariate data, using methods from artificial intelligence and information visualization. The tools is motivated by the problem of school redistricting, and is conducted in collaboration with staff from the Howard County (Maryland) Public School System. The approach developed in this project produces multiple similar solutions with respect to optimization criteria, but trying to ensure that the solutions are qualitatively different. The interactive environment allows the user to browse through "nearby" solutions, investigate minor perturbations of each solution, while reducing the number of critically different solutions. This interactive visual method is expected to be very effective in the school redistricting domain, resulting in a substantial reduction in the time to develop new redistricting plans, and a corresponding increase in the number of plans that can effectively be generated and compared. This will result improved school redistricting process, where the visual tools will improve the ability of the school system to explain, justify, and disseminate proposed plans and the associated quantitative evaluation. The methods developed in this project will also be applicable in almost any other domain where multidimensional spatial data need to be partitioned according to some criteria. The project's Web site (http://maple.cs.umbc.edu/redistricting/) will be used to disseminate the results of this research.
|
1 |
2007 — 2012 |
Bayles, Taryn Rheingans, Penny Morrell, Claudia Wolff, Michele Everhart, Amy |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Scholarships in It & Engineering @ University of Maryland Baltimore County
Interdisciplinary (99)
This project provides scholarships and supplemental academic and student support services for twenty (20) low-income, academically talented, full-time undergraduate students in General Engineering, Chemical Engineering, Mechanical Engineering, Computer Engineering, Computer Science, and Information Systems. Recruitment comes from transfer, new, and current students declaring a major in engineering or IT. The project will emulate an existing successful program infrastructure, which currently maintains a 94% retention rate for its undergraduates, provides enrichments that build a strong cohort, and builds upon lessons learned from a previous CSEMS program. The following project objectives include: (1) continuing to improve the transfer process between the state's two-year institutions and the current institution by targeting engineering and IT students; (2) building on the institution's demonstrated success in recruitment and retention programs supporting women and minorities; and (3) providing enhanced financial, academic, career and leadership development opportunities to the target audience.
|
1 |
2008 — 2011 |
Olano, Marc Sparling, Lynn (co-PI) [⬀] Desjardins, Marie (co-PI) [⬀] Rheingans, Penny Gobbert, Matthias [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Mri: Acquisition of An Interdisciplinary Facility For High-Performance Computing @ University of Maryland Baltimore County
Proposal #: CNS 08-21258 PI(s): Gobbert, Matthias K.; desJarding, Marie E.; Olano, Thomas M.; Rheingans, Penny; Sparling, Lynn Institution: University of Maryland - Baltimore County Baltimore, MD 21250-0002 Title: MRI/Acq.: Acq. of an Interdisciplinary Facility for High-Performance Computing
Project Proposed: This project, creating a high-performance computing cluster with many nodes and a state-of-the-art InfiniBand interconnect, aims to form the central resource for high-performance computing dedicated to support interdisciplinary research and training across the entire campus. Supporting 23 researchers and ten departments and centers, the cluster provides an opportunity to foster an emerging community of interdisciplinary researchers interested in computational science. The facility enables the solutions of scientific problems several magnitudes larger than currently possible. The project brings together researchers from Computer Science, Mathematics, Physics, Biology, Statistics, Economics, and Engineering (Electrical, Mechanical, Civil, and Environmental). In the earth sciences, the research helps to reduce uncertainty in policy decisions about water quality in the Chesapeake Bay watershed, allows analysis of terabyte-scale data sets for understanding climate change and its natural anthropogenic origins, and provides insights into the predictability of hurricane intensity. In Engineering, the research helps to assess the reliability of short-pulse laser systems in communication systems. Biomedical research tries to establish the links between protein structure and disease, and also directions in the design of biomaterials. Applications in geodesy and geomagnetic data assimilation improve understanding of the Earth?s gravitational field and basic processes within the Earth?s core. The basic research in visualization of complex systems, the development of efficient parallel algorithms, and the utilization of graphics hardware for scientific computing all have a broad range of practical applications.
Broader Impacts: Improving research collaborations, the instrumentation should lead to scientific and technical advances, have deep impact in education and research training of all users, and contribute to workforce training. Moreover, the increased collaboration and communication makes it easier to involve undergraduates and underrepresented groups in the area in this undergraduate and minority-serving institution.
|
1 |
2010 — 2014 |
Desjardins, Marie [⬀] Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Gv: Eager: Innovative Analysis and Visualization Approaches For Understanding Model Uncertainty @ University of Maryland Baltimore County
This exploratory project strives to develop a new approach to support human understanding of the uncertainty that is inherent in the structure and predictions of complex models.Specifically, the focus in the project is on understanding several types of uncertainty that are associated with model predictions. Sample uncertainty occurs when regions of the instance space are not well represented in the training data, and predictions are therefore based on sparse information. Model instability occurs when model predictions vary, depending on the training data that was used to construct the model. Prediction variability occurs when a given observation may have noisy attributes, and this input uncertainty leads to uncertainty in the model's predictions. Novel analytical techniques are developed to create meta-models that characterize these three forms of uncertainty. To facilitate user understanding of the nature and distribution of these multiple types of uncertainty across the model space, novel visualization methods represent these meta-models in a display space. Finally, a novel evaluation methodology is used to measure whether, and in what ways, important characteristics of the meta-models are captured in the visualization display space.
This work develops novel techniques in the fields of machine learning and data visualization. Contributions in machine learning include more powerful methods for constructing and analyzing meta-models that characterize multiple types of uncertainty associated with predictive models. Data visualization research focuses on new approaches for representing multi-valued, probabilistic, and complex data, enabling the display of the nature and range of model predictions and uncertainty. An interdisciplinary contribution is the development of a novel methodology for evaluating the quality of model visualizations with respect to the preservation of important model and meta-model characteristics.
The broader impacts of this project may be grouped into three major clusters: a new model building paradigm; fostering scientific collaboration; and integrating research and education. The results are expected to provide foundations for further research is management of uncertainty in deriving models representing a wide range of phenomena. This project lays a technical groundwork that can contribute to new collaborations between the PIs and application domain experts, facilitating broad interdisciplinary collaborations. Project results will be widely disseminated via the project web site (http://maple.cs.umbc.edu/complexmodels/). Finally, through teaching and training activities, this research project is also well suited to include the introduction of undergraduates to the possibilities of research and the incorporation of project topics into the PIs' courses on visualization and artificial intelligence.
|
1 |
2011 — 2017 |
Oates, Tim (co-PI) [⬀] Lutters, Wayne (co-PI) [⬀] Ellis, Erle Finin, Timothy (co-PI) [⬀] Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cdi-Type Ii: Globe: Evolving New Global Workflows For Land Change Science @ University of Maryland Baltimore County
This project focuses on Land Change Science (LCS).
Land Change Science is an emerging field of study, aimed at understanding interactions among human systems and the terrestrial biosphere, atmosphere and other Earth systems as mediated through human use of land. Advances in LCS are needed to better quantify, predict, mediate, and adapt to global climate change, biodiversity loss, and other consequences of land use and land cover change.
Despite vigorous efforts by a broad array of social and natural scientists, the cross-scale synthesis of multidisciplinary observations, models and theories on coupled human and natural systems (CHANS) that are required to advance LCS has yet to emerge. A major obstacle is the tremendous challenge in global integration and synthesis of local and regional CHANS case studies. This project will accelerate the emergence of new global workflows in land change science through GLOBE: an online collaboration environment combining quantitative real-time global relevance assessment, geovisualization, social-computational structures and machine learning algorithms. This will be accomplished in collaboration with international LCS institutions and experts, enabling researchers and institutions to rapidly share, compare, and synthesize local and regional studies by combining these with global datasets for human and environmental variables using a combination of machine learning, advanced visualization, semantic analysis and social networking.
The project has four core objectives that will be achieved through three integrated activities, as follows:
Objective 1: Create an online collaboration environment leveraging real-time global relevance analysis, geovisualization and social-computational knowledge generation towards the generation and sharing of new global workflows for land change science. Objective 2: Understand how to build effective social media tools organized around structured and informal scientific workflows. Objective 3: Develop evaluation methods and metrics and use them to demonstrate the utility of workflow-based social media tools in the context of scientists testing LCS hypotheses. Objective 4: Leverage GLOBE to characterize and optimize global knowledge generation in LCS.
To achieve these goals, this team will engage in the following activities:
Activity 1: Develop the social-computational infrastructure for GLOBE. Activity 2: Establish GLOBE as a means for social-computational knowledge generation. Characterize, share and optimize knowledge generation workflows for global synthesis and collaboration across CHANS studies and data collections. Activity 3: Test hypotheses and identify new research opportunities.
To understand anthropogenic global changes in the Earth system, scientists must generalize globally from observations made locally and regionally. This project will make fundamental hypotheses on the nature of human interactions with earth systems more readily testable by scientific methods, enabling major advances in land-change science and theory. Moreover, this project will engage the computing and social sciences in developing interactive online tools for scientific collaboration and data synthesis that will help identify knowledge gaps in LCS science. The tools will result in new ways of visualizing, communicating, connecting, comparing and synthesizing observations and models of land change processes at global, regional and local scales. Empirical investigation of GLOBE in use will advance our understanding of scientific collaboration more generally.
Broader impacts This project will develop, enhance and support long-term research collaborations across a broad set of scientific disciplines. It will support education and skill building for interdisciplinary collaboration by seasoned faculty, postdoctoral researchers, graduate students and undergraduate students. The project will design, host and disseminate advanced tools for cross-scale data and knowledge sharing, synthesis, and design of globally representative observing systems. By creating a new environment for sharing and integrating local knowledge, data and ideas across the social, biological and geophysical sciences, land change science will have greater potential to inform the sustainable stewardship of earth systems.
|
1 |
2012 — 2018 |
Bayles, Taryn Rheingans, Penny Desjardins, Marie (co-PI) [⬀] Seaman, Carolyn (co-PI) [⬀] Spence, Anne Blaney, Lee (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
A Community of Transfer Scholars in Information Technology and Engineering (T-Site) @ University of Maryland Baltimore County
The Community of Transfer Scholars in Information Technology and Engineering project at the University of Maryland Baltimore County is providing scholarships for 30 financially needy academically talented transfer students. The project is applying student support processes developed at the center for women in technology to build communities of learners for the transfer students. The project is providing academic and professional development information and opportunities for the scholars and is also fostering partnerships between the University of Maryland Baltimore County and several partner community colleges. Three cohorts of students are being selected and supported by the project. Students major in computer science, computer engineering, information systems, mechanical engineering or chemical engineering. Support structures include a new scholar's retreat as well as mentoring from faculty, peers and industry leaders. Academic and professional development activities along with transfer student seminars are provided to the scholars to help them graduate on time. The project is collecting information about the transfer student experience particularly of women and underrepresented minorities so that lessons learned can be used to increase the success of all transfer students, not only the scholars.
|
1 |
2012 — 2015 |
Martin, Susan Desjardins, Marie [⬀] Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Planning: Ce21 Maryland - Building Community and Knowledge to Increase Statewide Support For Computing Education @ University of Maryland Baltimore County
The University of Maryland, Baltimore County will gather data about the status of Computer Science education in Maryland high schools and build relationships among high school teachers, community college and university faculty, and state education administrators to facilitate and increase state-level support for lasting improvements to computing education. Despite the overall success of the K-12 education system in Maryland, opportunities to study computer science vary tremendously among the 24 school systems and approximately 200 high schools in the state. This disparity can be attributed to several factors, including the lack of a state-mandated computer science high school graduation requirement, the fact that there is no state-required teacher certification in the discipline, the absence of a standardized computer science curriculum, and barriers to entry for girls and underrepresented minorities.
In this CE21 Planning project, the interdisciplinary team at UMBC will focus on two main goals: (1) performing an assessment of the current state of high school computer science in each of the 24 Maryland school systems and (2) increasing knowledge about national issues associated with computer science education among high school and state administrators in Maryland, through state-wide summit meetings for teachers, administrators, and higher education faculty. The longer term objective of this work is to develop curriculum and teacher development programs that will improve the quality, breadth, and student diversity of computer science education in Maryland.
|
1 |
2012 — 2016 |
Seaman, Carolyn (co-PI) [⬀] Martin, Susan Desjardins, Marie (co-PI) [⬀] Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Tues: Transforming the Freshman Experience of Computing Majors @ University of Maryland Baltimore County
This project is developing, delivering and evaluating an innovative first-year seminar for computing majors that is designed to increase retention, completion, and success among students, especially women and those from underrepresented groups. Elements from successful first-year engineering courses, introductory computing courses, general first-year seminars, and the new AP CS Principles course combine to create a seminar that provides new computing majors with an overview of the discipline, foundational technical skills, a group design experience, and relevant professional development. A team of computing professors, staff with student affairs experience, and undergraduate students is delivering this highly interactive and learner-centered course. Peer mentors facilitate cohort building and provide informal advice, while undergraduate peer teachers directly support learning. The project is developing a novel course model and materials that are grounded in the research literature on student success, developing a framework for peer-led team learning in introductory courses in computing disciplines, and performing a thorough assessment of the outcomes of these activities. This intervention is improving the pedagogy in beginning computing and technology courses by assessing interventions that increase the retention and success of students in general, and women and underrepresented minorities in particular, resulting in a larger and richer pool of talent to solve important problems. The project also includes regional workshops to share course materials and insights with computing faculty from local two and four- year institutions.
|
1 |
2015 — 2020 |
Rheingans, Penny Seaman, Carolyn (co-PI) [⬀] Martin, Susan Laberge, E F Charles Ireland, Danyelle |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Creating a Community of Transfer Scholars in Computing and Information Technology @ University of Maryland Baltimore County
The "Creating a Community of Transfer Scholars in Computing and Information Technology" project at the University of Maryland Baltimore County (UMBC), will recruit, retain, and graduate a total of 25 academically-talented transfer students with financial need in computing majors. Students majoring in four computing majors in two academic departments (Computer Science, Computer Engineering, Information Systems or Business Administration Technology) will be eligible to participate in the program. The recruitment plan leverages existing collaborations with five community colleges in Maryland and will focus on encouraging woman and underrepresented minorities Students to apply to the scholarship program. The project will leverage a highly effective student support delivery model (CWIT Scholars Program) and the peer support of its community of undergraduate technology and engineering scholars.
Scholarship recipients will participate in a new scholars retreat; faculty, peer, and industry mentoring; academic and professional development activities; and a transfer student seminar. The project will contribute to the body of knowledge about the experiences of and challenges faced by transfer students in computing majors, especially women and underrepresented minorities, and will provide evidence regarding the efficacy of support services and community support on the success of transfer students in general and more specifically, students with financial need, women, and underrepresented minorities in computing majors.
|
1 |
2015 — 2017 |
Chen, Jian [⬀] Saper, Craig (co-PI) [⬀] Steiner, Karl (co-PI) [⬀] Rheingans, Penny Summers, Michael (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Mri: Acquisition of Pi2, a Cave2-Inspired Display For Discovery Science, Creativity, and Education @ University of Maryland Baltimore County
A hybrid-reality environment, named PI2, is expected to become an integral and vital part of a long-term vision for complex data analysis at this institution, in effect, a human-computer symbiosis in which humans guide computers to identify features of potential interest that the computer then locates and displays. Developing this vision requires advances in multiple areas, including semi-automatic feature detection, visual representations, and interaction, where traditional display modalities limit what can be displayed and perceived. Moreover, the instrument also facilitates broad interdisciplinary research and provides an innovative teaching and research environment for a diverse student population. It contributes to train future generations of researchers in state-of-the-art interactive visual computing for data analysis and enables broader activities and courses and expands research to outreach new applications (e.g., digital humanity for the American Indian population by working with the Smithsonian Museums). Expectations include: - Advancing multiple avenues of creative inquiry currently blocked or severely restricted will advance rapidly. (The instrument encourages visual thinking among researchers in sciences, healthcare, biomedicine, national security, humanities, and education); - Establishing appropriate levels of technologies needed for different classes of knowledge discovery analysis; and - Assembling a set of research projects to investigate the use of the instrument with the expectation of creating a novel, demonstrably useful, rich, and expressive set of techniques for many cyber-physical and cyber-human systems.
PI2, a hybrid-reality environment, aims to support research in interactive computing and digital humanities. The omni-stereo and mono-modalities of the instrument breaks the traditional barriers between virtual reality (VR) and tiled wall displays. The ability of PI2 to synthesize, capture, create, and analyze visual information in unprecedented detail can transform the way analysts interact with visual information. Leveraging many important characteristics: immersion, hybrid reality, high resolution, large field of view, large space and size, body-centric human-computer interaction, and support for heterogeneous data fusion, it benefits multiple projects in research areas (e.g., brain connectome, woodland ecology, interpersonal experiences, biomedicine, universal access, engineering physics, simulations, systems biology, education, digital humanities, green technologies, and unmanned-vehicle studies). The instrument brings together disparate fields: natural language processing, wearable computing, visualization, data mining, and interaction.
|
1 |
2016 — 2020 |
Ireland, Danyelle Seaman, Carolyn (co-PI) [⬀] Spence, Anne Martin, Susan Blaney, Lee (co-PI) [⬀] Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Developing, Implementing, and Evaluating a Post-Transfer Pathways Program For Computing and Engineering Majors @ University of Maryland Baltimore County
This project will establish and evaluate a program to support community college transfer students in their quest for a 4-year STEM (computing and engineering) degree. Determining how to reduce the loss of STEM community college students from 4-year programs attacks a significant problem in producing a STEM workforce. The project will also create and evaluate a more formal structure for collaboration between a 4-year institution and six community colleges. The project will accomplish its goals with an experienced interdisciplinary research team complemented by two consultants who are experts on diverse community college transfer students, especially women in STEM.
The project will generate empirical evidence about the impact of innovative models of transfer success coaching and a first year seminar on the transition as well as academic success and retention of transfer students majoring in computing and engineering from community colleges to research universities. It will also generate new knowledge about the use of inter-institutional collaboration structures and their impact on two and four-year institutions' efforts to improve the experiences and success of transfer students in computing and engineering majors.
|
1 |
2022 — 2028 |
Klein, Vanessa Turner, Roy Dufour, Christopher Yoo, Terry Rheingans, Penny |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Increasing Retention and Success of Computing Students Through Curriculum Development, Community Support, and Service Learning
This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Maine, a university with a high percentage of students who are first-generation and come from rural regions. Over its six-year duration, this project will fund scholarships to 30 unique full-time students who are pursuing bachelor’s degrees in computer science. First- year students will receive up to four years of scholarship support and transfer students will receive up to three years of support. This project aims to increase student persistence in computer science by linking scholarships with effective supporting activities, including a summer bridge program; faculty, peer, and industry mentoring; academic and professional development activities; a living-learning community; and seminars on first-year success, professional skills, and leadership. Curriculum changes will be made to improve first-year student retention and students’ overall career-readiness. Participating students will have the opportunity to use their computing skills to improve local communities through service learning activities. This project will develop and evaluate a student success infrastructure that can serve as a model for programs at other University of Maine System universities, as well as universities in rural states across the nation. Computing graduates with the ability to solve important and complex problems will benefit the regional and national economy.<br/><br/>The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. The first project objective is to establish a support structure and community-building scholar model for computing majors to help them succeed. A second objective is to provide academic and professional development information and opportunities for the scholars. The third and final objective is to strengthen relationships with community organizations to expand the pipeline of students pursuing computing majors, especially women, students from group underrepresented in their study of computing, first-generation students, and those from rural backgrounds. This project will examine the barriers to student success and effective strategies to reduce them. However, there is a lack of sufficient research about how programs combining academic supports, mentoring, professional skill development, and service learning can successfully help such students in overcoming these barriers. The specific research aims are to: (1) contribute to understanding of low-income students in computer science fields by examining student perceptions of barriers to pursuing computing degrees; (2) determine student perceptions about whether the project activities can mitigate these barriers; and (3) generate evidence about the impact of support services and community support on student success. A naturalistic inquiry research design will be used to collect and analyze data from focus groups, interviews, and surveys. This project will be evaluated using a participatory approach to gather information from administrative data, focus groups, interviews with key institutional supports, and surveys of students, graduates, and institutional partners. Results of this project will be made available through national professional conferences on computing education, regional meetings of technology educators, a project website, and regional media. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, transfer, graduation, and academic/career pathways.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|
0.972 |