PACE Canada College

Longstanding inequities exist in community colleges across the United States. To address these inequities, California Community Colleges educators have engaged in a variety of practices including the writing of equity plans and participation in equity data inquiry. However, there is an urgent need for greater focus on racial equity and for more faculty involvement in equity work at the classroom level. This paper presents a teacher case study exploring Classroom Equity Data Inquiry (CEDI), a tool for faculty professional learning focused on equitable student outcomes. In CEDI, professors examine their disaggregated classroom data, reflect on their class equity gaps, and pursue relevant professional development. They implement targeted interventions and then assess those interventions. This paper describes the author's sustained CEDI utilizing six years of equity data in her English as a Second Language classes at a small northern California community college. First, it provides a definition and rationale for CEDI. Second, it details the author's CEDI process and challenges. Third, it shares the author's changes in thinking and practice including high impact interventions the author implemented to reduce equity gaps for men of color in her classes. Fourth, the article describes positive qualitative student data and increased success and retention rates for Hispanic and multi-race males following the interventions. The article concludes that CEDI requires training, support, and time, but that the approach merits further research. More research is needed on CEDI methods and their possible impact on racial equity in the classroom.

Community colleges provide a beneficial foundation for undergraduate education in STEM majors. To inspire community college students to pursue a major in STEM, it is crucial to adapt strategies that help facilitate this interest. With support from the Department of Education Minority Science and Engineering Improvement program (MSEIP) and the Hispanic-Serving Institution Science, Technology, Engineering and Mathematics (HIS STEM), an internship program with multiple colleges was developed between community colleges and a public four-year university to engage community college students in cutting-edge engineering research. In the summer of 2017, four community college students participated in a ten-week electrical and computer engineering research internship project at a four-year university research lab. The summer internship project aimed to develop a real-time handwritten digit recognition system leveraging Neural Networks and Nvidia's Jetson Tx1 platform. Utilizing a modified Nvidia workflow, a robust digit recognition algorithm was designed using two industry standard programs for deep learning -- Tensor Flow and DIGITS. Nvidia's live image recognition demonstration created the framework to interface a camera module that sends images to the input of the digit classifying network in real-time. The student interns designed experiments to test the robustness of the algorithm in their daily environment, from low light situations to cluttered backgrounds with the handwritten digit blending in. The internship project created a stimulating environment for student interns to gain research experiences and learn a wealth of knowledge in deep learning, real time pattern recognition systems and leading-edge hardware platforms. The experiences contained within the ten-week internship allowed the interns to drastically improve technical writing and presentations, experimental design, data analysis and management, teamwork, and perseverance. The ten-week research internship was an effective method for engaging aspiring community college students by teaching the tools and methodology for success within an engineering profession.

In the current practice, sensors such as the accelerometers and strain gages are attached to or embedded into structure to measure its response for structural health monitoring purposes. However, installation and maintenance costs of these sensors are high, and the process is time and cost consuming partially because the operation of the structure has to be interrupted to perform the maintenance. Acoustic sensor with its ability to capture sounds through air vibration is very promising means to perform the task of measuring vibration for structural health monitoring purpose as a non-contact, non-destructive method. However, challenges remain on developing proper algorithms to convert measured acoustic data to vibration measurements from the source. The XXX System, with its enrollment of approximately 2.5 million students, is in a prime position to grow the future science, technology, engineering, and mathematics (STEM) workforce. Through a U.S. Department of Education funded XXXXX program between XX, a Hispanic-Serving community college and XXXX, a public comprehensive university, a 10- week summer program is set up to provide opportunity for community college students to experience the excitement of the state-of-the-art research. As one of the Civil Engineering projects in this summer program, the community college students are working closely with graduate students at XXXX to investigate the possibility to use acoustic sensor for non-destructive structural health monitoring. After learning basic theory through a series of training workshops, the students performed experimental testing with an array of microphone sensors with various configurations on a single-degree-of-freedom (SDOF) structure exciting on a shake table. With the acquired data, several post-processing algorithms are proposed to extract the useful information and eliminate the noise. In addition to the surveys, the participants were invited to participate in a 30-minute conversation about their summer internship experience to examine the internships' impact on interviewees in terms of: i) engineering self-efficacy and commitment to engineering as a career; ii) academic goals, including interest in research; iii) career goals; and iv) network of/engagement with professionals from academia and industry. The feedback from students shows that the XXXXX program offers an effective way to engage students from community college in engineering research.

Undergraduate research experience has been identified as an effective approach for engaging science, technology, engineering, and mathematics (STEM) students and increasing their retention rates. Community colleges enroll almost half of the nation's undergraduate students and play a significant role in STEM education. Thus it is important to develop strategies to provide community college students with research opportunities and experiences. With support from the Department of Education Minority Science and Engineering Improvement Program (MSEIP), a cooperative internship program between a community college and a public comprehensive university has been developed to engage community college students in leading-edge engineering research. In summer 2017, five sophomore students from the community college participated in a ten-week computer engineering research internship project in a research lab at the four-year university. This internship project aimed to develop a low-cost, portable, and flexible human-machine interface for real-time gesture recognition. Electromyography (EMG) is a technique for measuring the electrical activity generated by skeletal muscles. EMG pattern recognition (PR) is an intelligent method for deciphering neuromuscular information from EMG signals to identify users' intended movements. The human-machine interface developed by the interns provides real-time processing speed and sufficient storage capacity for computationally complex EMG PR algorithms by integrating mobile and cloud computing techniques. Specifically, a mobile Android application was developed which provides easy interface with a commercial EMG sensing armband Myo (Thalmic Labs) and a modular software engine seamlessly integrating a variety of signal processing modules, from data acquisition through pattern recognition, to real-time evaluation and control. In addition, an interface between the Android application and the Amazon Web Services Cloud Server was built which allows real-time cloud computing and storage. Real-time experiments were conducted on able-bodied subjects for hand gesture recognition to evaluate the accuracy, response time, and usability of the developed system. The project provided a great opportunity for the student interns to gain valuable research experience in human-machine interfaces and to improve their skills in teamwork, time management, as well as scientific writing and presentation. It also helped the students strengthening their confidence and interest in pursuing a STEM profession.

With support from the US Department of Education through the Hispanic-Serving Institution Science, Technology, Engineering, and Mathematics (HSI STEM) program, four community college engineering students participated in a ten-week summer research internship program at XXXXXXXX in summer 2017. A popular seismic damping device, magneto-rheological damper, was investigated by the interns during the internship. By analyzing different numerical models of the dampers, existing large-scale damper tests were studied and the damper response under external excitation is reproduced using the computing program software. UQ Lab was then applied to experimental results to explore the uncertainties inherent to the damper modeling. The probabilistic distributions of model parameters were derived and studied for their effect on real-world applications. This paper presents the summer intern project findings. Through the integration of state-of-the-art structural engineering research into the internship, this program also enables the development of project management, time management and teamwork skills, strengthens students' knowledge in earthquake engineering, and prepares them for successful academic and professional careers. The internship program therefore provides valuable mentorship for community college students during their transition to a four-year college.