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In this paper we describe the historical background of the introductory course in Electric Circuits I, how it has been taught, and the different modifications this course has undergone for the past few years. We describe preliminary results of a new step-based method on student learning which has been applied at the University of Texas at El Paso (UTEP) to improve students’ understanding of the topics covered in this course, and describe the step-based tutoring System, dubbed Circuit Tutor, developed by researchers at the UTEP. The results indicate Circuit Tutor platform can be used as a self-learning tool according to survey answers from students and the increasing passing rate in the Circuits I course. 

Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method). 

This work in progress (WIP) paper shares experiences and lessons learned from the first three years in the development and implementation of a model to improve the preparation and transition of Hispanic STEM doctoral students into community college (CC) faculty positions by the Hispanic Alliance for the Graduate Education and the Professoriate (H-AGEP). This is a collaborative effort between the City College of New York (CCNY) and The University of Texas at El Paso (UTEP) in partnership with El Paso CC (EPCC), LaGuardia CC (LaCC), and Queensborough CC (QCC). The proposed model addresses the important need of recruiting more Hispanic faculty at CC who can serve as outstanding teachers, mentors and role models to students at CC. Over 50% of Hispanics start their college journey at a community college while less than 5% of faculty in higher education is from Hispanic backgrounds. Increasing the can increase the number of Hispanic who receive degrees from community college and who transfer to 4 year institutions to obtain degrees in STEM. Higher representation of faculty from Hispanic and other racial/ethnic groups on campus have a positive impact on underrepresented minority student’s success when measured in grades and course completions as well as retention and degree completion. The lessons learned came from a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis performed as part of a self-study conducted in December 2020. The study included H-AGEP fellows, CCNY and UTEP participant faculty, dissertation advisors, and CC faculty mentors. The lessons learned provide important feedback for program improvement as well as information to teams who may be interested in developing alliances and collaborations with similar goals. A key result of the assessment is the value that CC partners bring in supporting teaching training and in providing a positive perspective on careers at community college to the participating doctoral students. The paper presents a brief summary of the H-AGEP model. Then it summarizes the findings from the self-study and concludes with the lessons learned from the process. 

This work in progress paper presents an overview of the Hispanic Alliance for the Graduate Education and the Professoriate (H-AGEP) program. H-AGEP is working on developing and implementing a new model to improve the preparation and transition of Hispanic STEM doctoral students into community college faculty positions. The partnership is a collaborative effort between the City College of New York (CCNY) (lead institution) and The University of Texas at El Paso (UTEP) along with a group of partner community colleges: LaGuardia Community College, Queensborough Community College, and El Paso Community College. The H-AGEP model consists of three main elements: (1) a training and mentoring program for effective STEM teaching at community colleges; (2) a training program for effective mentoring of community college students in STEM research; and (3) a professional development program to address career preparation, transitioning, and advancement at academic careers in community colleges. H-AGEP research goals are: (1) to consider the collected evaluation and research data to determine what intervention activities are most impactful, and (2) to better understand the career-decision making process of Hispanic STEM doctoral students regarding whether they will seek employment at community colleges and other two-year institutions. An interesting aspect of the partnership is that the institutions in El Paso, Texas, serve primarily a Mexican-American student population while the New York institutions serve primarily a Hispanic population of Caribbean origin. This provides the unique opportunity to compare Hispanic students from both groups. The program evaluation: (1) documents and provides feedback on H-AGEP activities and model implementation; and (2) assesses the extent to which H-AGEP is achieving its intended outcomes. Assessment results on the first cohort of students in the program show the value of including community college faculty as career and teaching mentors in the program. Furthermore, the effect of model interventions in students from the first cohort show positive advances in improving teaching skills, increasing student professional networks, and increasing interest and awareness in careers at community college. 

Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.