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2016 Implementation Grants

The College of Engineering and Information Technology has established an initiative that supports the four Strategic Plan Goals identified as target areas that support the COEIT Strategic Plan. These plans will be implemented during the 2016-2017 academic year. The awards include faculty and staff from all COEIT Departments and the Center for Women in Technology (CWIT).

During the first round of awards announced in Spring 2016, ten projects received funding totaling $229,283.

In Fall 2016, the next round of six awards totaling $183,252 were announced.

Grant awards have been made in the following categories:

Goal 1.4: Research Collaboration

An Autonomous Unmanned Aerial System for Environmental Health Monitoring
Award Date: Spring 2016
Principal Investigator: Andrew Gadsden

Proposal to Join the Human-Computer Interaction Consortium
Award Date: Spring 2016
Principal Investigator: Amy Hurst

Novel techniques to estimate dynamic brain connectivity based on big fMRI data
Award Date: Fall 2016
Principal Investigator: Seung-Jun Kim

Establishing an integrated experimental-computational pipeline for predictive modeling in cancer immunotherapy
Award Date: Fall 2016
Principal Investigator: Greg Szeto

Prototype Development for a Networked Multi-Modal Sensor System for Autonomous Non-intrusive and Self-sustaining Pipeline Monitoring
Award Date: Spring 2016
Principal Investigator: Mohamed Younis

Semantic Representation of Genetic Testing Data in OWL
Award Date: Fall 2016
Principal Investigator: Qian Zhu

Addressing Fundamental Sensing and Data Processing Problems in Sustainable Farms
Award Date: Spring 2016
Principal Investigator: Ting Zhu

Goal 1.5: Research Infrastructure

Upgrade to Tissue Culture Laboratory to Handle BSL-2 Organisms
Award Date: Spring 2016
Principal Investigator: Victor Fulda

Revitalizing the CoEIT User Studies Labs
Award Date: Fall 2016
Principal Investigator: Wayne Lutters

Emergency Power for Critical Research Equipment in the Engineering Building
Award Date: Spring 2016
Principal Investigator: James Milani

Research Infrastructure Enabling PCB Manufacturing Capabilities in COEIT
Award Date: Spring 2016
Principal Investigator: Chintan Patel

Acquisition of MicroLabBox Real-time Data Acquisition and Control Platform for Research and Education
Award Date: Spring 2016
Principal Investigator: Weidong Zhu

Goal 2.1: Graduate Programs

Enhancing Recruitment and Training of IS and HCC Ph.D. Students
Award Date: Spring 2016
Principal Investigator: Zhiyuan Chen

A Synergistic Approach to Improving Graduate Recruitment and Increasing Diversity in the Department of Chemical, Biochemical & Environmental Engineering
Award Date: Fall 2016
Principal Investigator: Chris Hennigan

Goal 2.2: Retention Rates

Celebrating Women in Technology – Expanding the CWIT Affiliates Program
Award Date: Spring 2016
Principal Investigator: Crystal Diaz-Espinoza

Bridges and Guided Review Sections: Improving Retention In and Beyond CMSC 201
Award Date: Fall 2016
Principal Investigator: Katherine Gibson

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Project Abstracts

Goal 1.4: Research Collaboration
An Autonomous Unmanned Aerial System for Environmental Health Monitoring

Andrew Gadsden (PI), Cynthia Matuszek, Lee Blaney

Abstract:
This project, a collaborative effort across COEIT, includes three Assistant Professors from the Departments of Mechanical Engineering (Dr. Gadsden), Computer Science and Electrical Engineering (Dr. Matuszek), and Chemical, Biochemical & Environmental Engineering (Dr. Blaney). The research project aims to develop a tethered unmanned aerial system (UAS), outfitted with high-resolution sensors, for environmental health monitoring and precision agriculture. It aims to develop semi-automated “hotspot” analysis techniques that provide survey personnel with real-time information identifying unhealthy environments or pollution sources. The first test for the system will be in the area of shoreline surveys of the Chesapeake Bay. The research team has partnered with the Maryland Department of the Environment (MDE). MDE is particularly interested in this technology and corresponding application because the department is responsible for safeguarding water quality in this area, including the domain of the local shellfish industry. The proposed system will be developed as a prototype and used to collect shoreline data in an effort to detect sources of pollution entering the Chesapeake Bay. The most common pollution source is failing septic tanks from residential properties. This collaborative project will improve both scientific knowledge and technical capabilities in the broad areas of environmental health monitoring, unmanned systems, and robotics. In particular, the following areas will be advanced or included: autonomy, control and estimation theory, information fusion, obstacle avoidance, vision, water sampling and analysis, and USDA/FDA best practices (for national shoreline surveys).

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Proposal to Join the Human-Computer Interaction Consortium

Amy Hurst (PI), Andrea Kleinsmith, Anita Komlodi, Ravi Kuber, Wayne Lutters, Helena Mentis

Abstract:
The UMBC Interactive Systems Research Center (ISRC) has received funding to join the Human-Computer Interaction Consortium (HCIC) and attend their annual consortium. This organization’s mission is to foster interaction among universities, industries, and government research laboratories in the area of Human-Computer Interaction through networking at their annual consortium. Through joining this consortium, faculty and students in the ISRC will have the opportunity to present their research and interact with senior researchers in this field.

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Novel techniques to estimate dynamic brain connectivity based on big fMRI data

Seung-Jun Kim (PI), Hamed Pirsiarvash, Tulay Adali

Abstract:
The goal of the proposed research is to lay the groundwork for developing novel estimation techniques of time-varying network connectivity of the human brain by using large-scale functional magnetic resonance imaging (fMRI) datasets. Departing from the more established mode of fMRI analysis based on static assumptions, our focus will be on designing algorithms that can capture the dynamic variability of the relevant brain regions in the connectivity analysis, as well as the connectivity links themselves. Such an analysis has become possible recently thanks to the availability of large-scale fMRI data sets, which allow reliable estimation of brain activities in greater detail and finer granularities. We will develop methods based on latent variable models and deep learning approaches and explore synergistic combination of their complementary strengths. Our preliminary results from the data-driven techniques will be validated by our external collaborators in neuropsychiatry and brain imaging. The timely research topic and the interdisciplinary collaboration between the players in traditionally distinct fields, namely, signal processing (Electrical Engineering) and computer vision (Computer Science), in addition to the tight collaboration with the experts in medicine will forge a strong advantage for significant external funding.

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Establishing an integrated experimental-computational pipeline for predictive modeling in cancer immunotherapy

Greg Szeto (PI), Jian Chen

Abstract:
This proposal will support an emerging collaboration to establish an integrated experimental-computational pipeline. The proposed project will create predictive models of cancer immunotherapy using the Luminex FlexMAP3D instrument for experimental measurement of proteins in both mouse and human tumor therapy samples (Szeto, UMBC). Simultaneously, data visualization tools will be developed for hypothesis generation and analyzing data structures (Chen, UMBC). This work will directly impact Goal 1.4 by supporting a new collaboration between CBEE and CSEE, including co-advised grad student, as well as collaboration with external partners including multiple myeloma patients (Ivan Borrello, JHMI) and preclinical studies (Eduardo Davila, UMB). Predictive modeling of cancer therapy falls under the Grand Challenge: Engineer Better Medicines. Goal 1.5 will be impacted by enhancing bioanalytical capability on-campus, providing a novel pipeline for multiplexed bioassays and data visualization/analysis that will serve as a hub for researchers in both experimental and data sciences. Models and tools will be published and made publicly available by the end of year 1; the preliminary data will form the basis of an external proposal to NSF IIS in the winter. Iterative design will be used to allow experiment to influence visualization and vice-versa. Experimental outcomes include new insights and models indicating biomarkers that can predict immunotherapy efficacy; these can guide clinical decision-making to increase patient survival. Data visualization outcomes include tools that enhance the efficiency of hypothesis generation and enable unique insights from experimental data, increasing experimentalists’ ability to interpret “big data.”

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Prototype Development for a Networked Multi-Modal Sensor System for Autonomous Non-intrusive and Self-sustaining Pipeline Monitoring

Mohamed Younis (PI), Soobum Lee, Seung Jun Kim, Weidong Zhu

Abstract:
Our society relies on extensive pipeline networks to deliver water to households, crude oil from the field to refineries, and petroleum products such as gasoline to storage depots. Any interruption in the delivery due to clogs or leakage can cause major economic impact and increased inconvenience to citizens. Leakage not only wastes resources but can also be environmentally hazardous. Therefore, detecting and localizing leakage in a timely manner are very important for containing potential negative effects. Current pipeline monitoring systems are inefficient and costly, and usually involve operators in the process. This project opts to overcome these shortcomings by developing an autonomous cyber-physical system for remote detection and localization of leaks in pipelines.

The proposed system employs non-intrusive monitoring methods in order to avoid disrupting normal pipeline operation, facilitate maintenance of the sensor nodes, and enable applicability to existing pipelines. The system will be composed of a networked set of sensor nodes that are clamped on the outer wall of pipes in carefully selected locations throughout the pipeline. Multi-modality sensing is exploited in order to deal with fluctuations of the flow rate and fluid characteristics, determine the scope of the leakage, and achieve high-fidelity assessment of the overall health status of a pipeline.

The node design includes a radio transceiver, a battery, an energy harvester and sensor(s) in the form of an acoustic-emission detector, an accelerometer, and an ultrasonic transducer. The acoustic-emission detector and vibration sensors probe the effect of leaks on the pipe structure to detect the noise and the shake that result from liquid spill through a crack. The ultrasound transducer tracks the flow rate and interprets a drop in the flow volume as an indication of liquid loss through a crack in the pipe. The ultrasound transducer is further used to detect leaks based on the presence of air bubbles in the fluid. The data collected by the individual nodes are correlated locally and an aggregate report is sent over radio links via multi-hop routes to a base-station for further analysis. The sensor nodes operate autonomously and scavenge energy to sustain operation. We will exploit piezoelectric material to harvest energy from pipe vibration due to the fluid flow pressure, and plan to employ micro solar panels to take advantage of sunlight when available.

The research is interdisciplinary and involves expertise in embedded systems and networks (Computer Science and Electrical Engineering), vibration and solar energy harvesting (Mechanical Engineering), and signal processing (Computer Science and Electrical Engineering). The seed fund is to enable a multi-disciplinary team from the Computer Science and Electrical Engineering and Mechanical Engineering departments to build a lab-based pipeline prototype, collect data, and conduct preliminary analysis.

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Semantic Representation of Genetic Testing Data in OWL

Qian Zhu (PI), Karuna Joshi, Yelena Yesha

Abstract:
The objective of this proposal is to develop a computational resource, a Clinical evidence based Genetic Test Ontology (GTO) to provide comprehensive genetic test related information for individualized genetic test recommendation. We will integrate genetic evidence from existing resources and clinical evidence from EHR (Electronic Health Records), and semantically represent such information in a standard and logical form by using Ontology Web Language (OWL), a family of knowledge representation languages for authoring ontologies. Two innovations will be introduced by this proposed study. 1) Application of integrating clinical evidence extracted from the EHR and genetic evidence from well-known sources will accelerate pace of precision medicine; 2) Data representation in a logical and intelligent manner by applying OWL, will strongly support better data use, sharing and integration, ultimately as a data foundation for clinical decision support in genetic medicine.

This project will build close collaboration between faculty in IS and CSEE to address a challenging health problem in genetic medicine. Dr. Zhu will lead the whole project by integrating genetic evidence and clinical evidence to develop the cGTO; Dr. Joshi will co-lead the EHR data analysis and cGTO development given her extensive experience in working with EHR data and semantic web technology; Dr. Yesha, as a director of CHMPR, will help on data management of the cGTO and data consumption on the cGTO via semantic inference by using big data techniques.

The work will lay the foundation leading the success of obtaining external funding from NIH and NSF smart and connected health program.

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Addressing Fundamental Sensing and Data Processing Problems in Sustainable Farms

Ting Zhu (PI), Jianwu Wang

Abstract:
Growth of the global population has placed an ever-increasing stress on three key and interconnected resources: food, energy, and water. To take the first attempt for addressing this grand challenge, our project aims to address fundamental sensing and data processing problems in food, energy, and water nexus systems under the sustainable farms environment. To achieve this goal, the project will design and implement a novel real-time monitoring, prediction, and data analysis system for renewable energy harvesting, rainwater collection and irrigation, and soil moisture monitoring. It will not only better understand and model the nexus systems, but also provide intelligent control to save energy and water while keeping good food production. We will use preliminary results and a testbed from this project to apply for external grants from the National Science Foundation (NSF), Department of Energy (DOE), and other agencies. We will also combine the expertise and foster collaborations from Computer Science and Electrical Engineering, and Information Systems departments, integrating multiple techniques (including Sensor Networks, Internet of Things, Big Data, Machine Learning, and Scheduling Optimization) to target grand challenges related to water, energy and food. The project will increase COEIT investment in research infrastructure and support sharable to other faculties. The developed novel real-time monitoring, data processing and analysis system is expected to significantly improve the efficient usage of food, energy, and water as well as provide a platform for course project, graduate thesis, and to secure additional federal funding from NSF Cyber-Physical Systems program and Food, Energy, Water Nexus program, Department of Energy, and Department of Agriculture.

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Goal 1.5 Research Infrastructure
Upgrade to Tissue Culture Laboratory to Handle BSL-2 Organisms

Victor Fulda (PI), Jennie Leach

Abstract:
The goal of this project is to improve the current Tissue Culture Laboratory infrastructure to handle BSL-2 organisms and materials. To handle BSL-2 organisms, the following upgrades to research infrastructure are required: wall paint and ceiling tiles that can be disinfected, benches with wheels that can be disinfected and moved for cleaning, dedicated HEPA air filtration, and relocation of the gas cylinder cage to outside the laboratory. The proposed upgrades to research infrastructure will not only improve the safety and cost-effectiveness of ongoing research projects, but will also open doors for new interdisciplinary collaborations with entities in UMBC’s College of Natural and Mathematical Sciences, University Maryland, Baltimore, and industry.

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Revitalizing the COEIT User Studies Labs

Wayne Lutters (PI), Stacy Branham, Amy Hurst, Andrea Kleinsmith, Anita Komlodi, Ravi Kuber, Helena Mentis

Abstract:
This proposal seeks funds to revitalize the COEIT user studies labs (ITE 440-443) by upgrading existing equipment, refreshing the physical space, and increasing the visibility of the labs across campus and the community. These ISRC managed labs are essential for conducting the human-centered design and engineering research which spans multiple programs in the college. An investment in improving this research infrastructure (1.5) will also better align the labs in support of new faculty and curricular updates. It will enable new research collaborations (1.4) within the college (e.g., CSEE), across campus (e.g., Erickson School, Psychology, GES), and with industry partners. This activity will support a NSF CISE-CRI instrumentation proposal (submitted Fall 2016). Lessons learned from this revitalization effort will be shared with other labs and centers in COEIT.

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Emergency Power for Critical Research Equipment in the Engineering Building

Jim Milani (PI)

Abstract:
$15K was awarded from the COEIT Strategic Initiative Award Program with a match in excess of $15K from Facilities Management to install emergency back-up power capability to protect specimens, cell cultures, stock materials, antibodies, materials that were developed that would be difficult or impossible to duplicate. years of prior research, and current research (incubators, etc.). This provides backup to critical labs in Chemical, Biochemical & Environmental Engineering, Computer Science and Electrical Engineering, and Mechanical Engineering. The renovation has already been completed and took advantage of some excess power capacity on an existing emergency generator.

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Research Infrastructure Enabling PCB Manufacturing Capabilities in COEIT

Chintan Patel (PI), Nilanjan Banerjee, Ryan Robucci, Yordan Kostov, Nirmalya Roy, Ting Zhu, Charles LaBerge, Cynthia Matuszek

Abstract:
The proposed infrastructure will enable researchers and educators across COEIT to have access to in-house electronics prototyping. Cost and turn-around times of external printed circuit board (PCB) manufacturing hinder rapid prototyping and innovation. Collaborators currently encompass faculty members in Computer Science and Electrical Engineering (both Computer Science and Electrical/Computer Engineering), Information Systems, and Chemical, Biochemical & Environmental Engineering working on a diverse set of research projects. Having access to this infrastructure will nurture interdisciplinary collaboration and will lead to proposals for external funding directly to enhance the system as well as new research projects. The equipment procured under this proposal will have a direct impact on the Capstone course in Computer Engineering, which involves both Computer Engineering and Mechanical Engineering undergraduate students. This will improve the quality and the scale of the projects that the students can undertake in this vital senior design course. It will also enhance the experience of undergraduate and graduate students in several Computer Engineering/Computer Science classes related to electronics and robotics. We envision expanding this capability with external funding and enabling centers in assistive care, cyber-physical systems, biomedical sensors and systems, and cybersecurity.

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Acquisition of MicroLabBox Real-time Data Acquisition and Control Platform for Research and Education

Weidong Zhu (PI), Mohamed Younis, Panos Charalambides, Soobum Lee, Meilin Yu, Andrew Gadsden, Ting Zhu

Abstract:
This grant will be used to acquire the dSPACE MicroLabBox data acquisition and control platform with which to develop a series of novel measurement and control applications. The comprehensive functionalities of MicroLabBox will let an idea become an action much easier and faster. A simulation model can be easily modified and implemented on MicroLabBox.

The proposal will develop a continuous scanning laser Doppler vibrometer (CSLDV) measurement system for nondestructive structural damage identification. MicroLabBox will be used to control scan frequencies and acquire the vibration velocity of a damaged structure. Demodulation and polynomial methods will be applied to obtain operation deflection shapes (ODSs) of the damaged structure from the CSLDV velocity output. Curvatures of ODSs and curvature damage indices will be used to identify damage locations. The measurement system can provide a novel and efficient nondestructive structural damage identification method with high sensitivity and accuracy. This new method does not require any a priori information of associated undamaged structures and can be directly applied to structures that have damage in various forms. Another application of MicroLabBox is to measure wind speeds, rotation speeds and corresponding torques of a vertical-axis wind turbine in a wind tunnel test. Some other application of MicroLabBox can include investigation of different types of failures in microgrids.

MicroLabBox is one of the most practical tools to attract students’ attention. While control theory and digital signal processing are important courses for undergraduate and graduate students, the fundamental concept cannot be easily remembered and understood. Well-designed experiments using MicroLabBox will greatly enhance their understanding of theories and increase their interest in research. MicroLabBox has many applications in mechanical, electrical, automotive and aerospace industries, as well as in industrial automation. Hence, use of MicroLabBox in class will enable students to understand industrial standards in advance.

Acquisition of MicroLabBox will greatly improve UMBC’s research infrastructure, allowing its faculty and students to tackle challenging real-time control problems. It will enable cross-disciplinary collaborations among faculty from different departments in COEIT.

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Goal 2.1: Graduate Programs
Enhancing Recruitment and Training of IS and HCC Ph.D. Students

Zhiyuan Chen (PI), Anita Komlodi, Dongsong Zhang

Abstract:
This project has two aims: (1) increase both the size and quality of the pool of Ph.D. applicants for Information Systems (IS) and Human-Centered Computing (HCC) programs through activities such as holding a visit day, development of marketing materials, and recruitment at relevant conferences; (2) improve the academic training, quality, and placement of existing Ph.D. students and conduct data collection and analysis to discover both strengths and weakness of our programs. If this project is successful, it is expected to improve the enrollment and quality of IS and HCC Ph.D. programs and number of graduate applications. In addition to traditional recruitment efforts, we will also try more innovative means such as using social media to disseminate marketing material and holding a virtual visit day. The outcome of the project will be assessed by the number of applicants to both programs in comparison to the corresponding numbers in past years, the yield of the visit day (how many do enroll after visiting us), and surveys of students and applicants. Many of the planned activities have one-time startup cost and low maintenance cost. We will analyze the return on investment of activities with recurring cost and continue those with low cost but high return beyond the project period. We will also make our findings available to other departments in the College.

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A Synergistic Approach to Improving Graduate Recruitment and Increasing Diversity in the Department of Chemical, Biochemical & Environmental Engineering

Chris Hennigan (PI), Erin Lavik

Abstract:
We propose a multi-faceted approach to raise awareness of the CBEE graduate programs among domestic and international applicants and to increase the diversity in Ph.D. graduates from our department. To increase diversity among Ph.D. applicants and graduates from CBEE, we will build partnerships with two regional HCBUs – Howard University and Delaware State University. CBEE faculty will visit engineering departments at both schools in the fall. We will then host an Open House at UMBC for engineering students from both schools who are interested in graduate school. To build awareness of our graduate program among international applicants, CBEE faculty will visit a highly ranked university in India (IIT-Bombay) and Mexico (UNAM). These visitations will leverage existing collaborations between CBEE faculty and international partners. CBEE will also revive a past recruiting relationship with the University of Porto in Portugal. The proposed work will raise awareness of the unique opportunities for graduate study offered by CBEE among a highly qualified pool of potential applicants. We expect this work to increase the number of underrepresented minorities applying to and graduating from CBEE Ph.D. programs. We also expect to increase the number of CBEE Ph.D. students from our international partner institutions. These advances will increase research productivity in the department by increasing the graduate graduation rate and reducing the time to graduation. Overall, these improvements will help to raise the national profile of our department and COEIT as a whole.

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Goal 2.2: Retention Rates
Celebrating Women in Technology – Expanding the CWIT Affiliates Program

Crystal Diaz-Espinoza (PI)

Abstract:
The Center for Women in Technology (CWIT) will expand and restructure our existing Affiliates Program to increase our impact on undergraduate women in the college, including both the quantity of students served and quality of programs offered. The project will take place in three major stages: (1) a focus in Spring 2016 on yield, encouraging women accepted to COEIT majors to attend UMBC and become engaged with the Affiliates Program from the start, (2) offering a Summer 2016 experience for new and returning women in COEIT to build community, and (3) adding a Fall 2016 mini-conference for COEIT women to aid in retention efforts.

The CWIT Affiliates Program builds and implements programs based on empirically based and widely accepted best practices to increase retention of women in computing and engineering majors. Research reports and resources about best practices produced by The American Society of Engineering Education (ASEE), The National Center for Women & Information Technology (NCWIT) and the American Association of University Women (AAUW) suggest the following approaches for increasing the retention and graduation of women in computing: (1) facilitating positive student-student and student-faculty interactions, (2) community building, (3) peer mentoring, (4) living-learning communities, (5) programs specifically for first-year students, (6) promoting career awareness, and (7) incorporating speakers/event participants from industry.

The goal of this project is to increase the percentage of women in COEIT who are impacted by CWIT programs, in direct support of Goal 2.2 in the strategic plan: Increase the undergraduate and graduate graduation rates from the College. This project will also further Goal 2.4: Foster diversity and a climate of inclusive excellence. Currently, CWIT reaches approximately 38% of the women undergraduates in COEIT through our CWIT, Cyber, and T-SITE Scholars Program as well as our current Affiliates Program. The current level of active engagement by Affiliates varies greatly with some only registering and other participating in a number of events throughout the academic year. Through this project, we aim to increase the level and quality of engagement of registered Affiliates and increase CWIT’s contact to at least 50% of COEIT undergraduate women in CWIT activities by the end of the Fall 2016 semester.

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Bridges and Guided Review Sections: Improving Retention In and Beyond CMSC 201

Katherine Gibson (PI)

Abstract:
This project’s purpose is to increase the undergraduate retention rate for new freshmen in computer science and computer engineering using two complementary strategies:

  1. Implementing a lecture section specifically for students who have no programming experience. This lecture section will also have an additional hour each week of TA-led guided review.
  2. Implementing a one (1.0) credit bridge course during the Summer and Winter sessions. This bridge course is targeted at students who narrowly missed passing CMSC 201 with the grade necessary for their major’s gateway in the preceding Spring or Fall sessions.

The working assumption behind both strategies is the observation that there are students in these majors who are capable of completing the degree requirements, if they were better supported. This is especially true for students who struggle early in the curriculum. Often, students who are underprepared from high school can fall behind because of the university’s accelerated pace and higher workloads.

 

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