Title: Developing New Data Pathways in Community Colleges
As more industries come to rely on data collection and analysis, the demand for skilled data workers is growing at a pace that outstrips the capacity of colleges to develop the programs needed to produce qualified employees. Technician education programs and community colleges are expressing growing interest in developing data programs. What is missing are strategies and supports that can enable colleges to rapidly respond to these opportunities and scale-up their efforts to train the next generation of data workers in a sustained and timely manner. This panel will present EDC’s Data Pathway Development Process designed to support faculty in the creation and launch of new data programs at two-year institutions that are aligned to local employment opportunities. more »« less
Baumer, Benjamin S.; Horton, Nicholas Jon
(, Harvard Data Science Review)
Meng, X-L
(Ed.)
A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public 2-year colleges. While the number of 4-year colleges offering bachelor’s degrees in data science continues to increase, data science instruction at many 2-year colleges lags behind. A major impediment is the relative paucity of introductory data science courses that serve multiple student audiences and can easily transfer. In addition, the lack of predefined transfer pathways (or articulation agreements) for data science creates a growing disconnect that leaves students who want to study data science at a disadvantage. We describe opportunities and barriers to data science transfer pathways. Five points of curricular friction merit attention: 1) a first course in data science, 2) a second course in data science, 3) a course in scientific computing, data science workflow, and/or reproducible computing, 4) lab sciences, and 5) navigating communication, ethics, and application domain requirements in the context of general education and liberal arts course mappings. We catalog existing transfer pathways, efforts to align curricula across institutions, obstacles to overcome with minimally disruptive solutions, and approaches to foster these pathways. Improvements in these areas are critically important to ensure that a broad and diverse set of students are able to engage and succeed in undergraduate data science programs.
The Genomic Data Science Community Network
(, Genome Research)
Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.
Grinberg, I; Singer, J
(, Journal of engineering technology)
Engineering Technology (ET) programs at community colleges and colleges/universities play a vital role in meeting the growing national demand for STEM graduates. Many accredited ET programs feature design projects that allow students to apply content knowledge and gain valuable workplace skills. Undergraduate research, especially inquiry-based projects, helps students take ownership of their own learning and see the real-world relevance of research as they learn problem-solving skills. EvaluateUR-CURE, an evidence-based method developed at SUNY Buffalo, measures a broad range of desirable outcomes that include both content knowledge and outcomes that are critically important in the workplace, such as communication skills, creativity, autonomy, an ability to overcome obstacles, critical thinking, and problem-solving skills. EvaluateUR-CURE also provides students opportunities to develop metacognitive skills as a way to identify how much academic progress they have made or still need to make. This paper addresses the process of development of performance indicators and presents the results of assessment and evaluation of ETAC ABET student outcomes and outcome categories of EvaluateUR-CURE.
Rice, A.; Loikith, P.; Murry, A.
(, Transactions American Geophysical Union)
Opportunities for undergraduate research in STEM programs at community colleges can be few where lower-division science curriculum emphasizes classroom and laboratory-based learning and research laboratories are limited in number. This is particularly true in the geosciences where specialized programs are extremely rare. Urban serving academic research institutions have a unique role and opportunity to partner with regional community college programs for undergraduate research early-on in student post-secondary educational experiences. Programs built for community college transfer students to urban serving undergraduate programs can serve to integrate students into major programs and help reduce transfer shock. The benefits of exploring research as an undergraduate scholar are numerous and include: building towards mastery of technical skills; developing problem-solving in a real-world environment; reading and digesting scientific literature; analyzing experimental and simulation data; working independently and as part of a team; developing a mentoring relationship with a research advisor; and building a sense of belonging and confidence in a scientific field. However, many undergraduate research internships are targeted towards junior-level STEM majors already engaged in upper-division coursework and considering graduate school which effectively excludes community college students from participating. The Center for Climate and Aerosol Research (CCAR) Research Experience for Undergraduate program at Portland State University serves to help build the future diverse research community. 10-week intern research experiences are paired with an expert faculty mentor are designed for students majoring in the natural/physical sciences but not necessarily with a background in climate or atmospheric science. Additional programmatic activities include: 1-week orientation and training using short courses, faculty research seminars, and hands-on group workshops; academic professional and career development workshops throughout summer; journal club activities; final presentations at end of summer CCAR symposium; opportunities for travel for student presentations at scientific conferences; and social activities. Open to all qualifying undergraduates, since 2014 the program recruits primarily from regional (Northwest) community colleges, rural schools, and Native American serving institutions; recruiting students who would be unlikely to be otherwise exposed to such opportunities at their home institution. Over the past 9 cohorts of REU interns (2014-2019), approximately one third of CCAR REU scholars are community colleges students. Here we present criteria employed for selection of REU scholars and an analysis of selection biases in a comparison of students from community colleges, 4-year colleges, and PhD granting universities. We further investigate differential outcomes in efficacy of the REU program using evaluation data to assess changes over the program including: knowledge, intrinsic motivation, extrinsic motivation, science identity, program satisfaction, and career aspirations. In this presentation, we present these findings along with supportive qualitative analyses and discuss their implications for community college students in undergraduate research programs in geosciences.
The U.S. bioeconomy has been estimated to be $950 billion and growing [1]. Sustaining this growth requires a skilled workforce who can manufacture goods developed through biotechnology. Scaling the biotechnology workforce to the needed level requires the ability to measure its size. The National Center for Education Statistics (NCES) is the federal agency responsible for gathering education data in the U.S. Colleges that receive federal funding are mandated by law to report data every year to the NCES. Given the comprehensive nature of these data, we sought to determine whether it could be used to measure the number of certificates and degrees in biotechnology awarded by two-year colleges. An unexpected challenge was the requirement by the NCES data retrieval page for Classification of Instructional Program (CIP) codes and the inconsistent use of CIP codes by college biotechnology programs. We were able to circumvent these challenges by using data from the InnovATEBIO National Center for Biotechnology Education. InnovATEBIO data allowed us to identify two-year colleges with biotechnology programs and use those results to learn which CIP codes were being assigned. Knowing the CIP codes and their use in different states supplied the information we needed to obtain certificate and degree completion data from the NCES. These data provided insights into the changing numbers and demographics of biotech students during the past twenty years. Not only are these data important for understanding trends in biotechnology education, they are imperative for guiding the initiation, development, and sustainability of biotechnology education programs at two-year colleges.
Malyn-Smith, J., MacGillivray, S., Harris, M., and Polzin, J. Developing New Data Pathways in Community Colleges. Retrieved from https://par.nsf.gov/biblio/10297829. Proceedings of SITE Interactive 2020 Online Conference 2020.1
Malyn-Smith, J., MacGillivray, S., Harris, M., & Polzin, J. Developing New Data Pathways in Community Colleges. Proceedings of SITE Interactive 2020 Online Conference, 2020 (1). Retrieved from https://par.nsf.gov/biblio/10297829.
Malyn-Smith, J., MacGillivray, S., Harris, M., and Polzin, J.
"Developing New Data Pathways in Community Colleges". Proceedings of SITE Interactive 2020 Online Conference 2020 (1). Country unknown/Code not available. https://par.nsf.gov/biblio/10297829.
@article{osti_10297829,
place = {Country unknown/Code not available},
title = {Developing New Data Pathways in Community Colleges},
url = {https://par.nsf.gov/biblio/10297829},
abstractNote = {As more industries come to rely on data collection and analysis, the demand for skilled data workers is growing at a pace that outstrips the capacity of colleges to develop the programs needed to produce qualified employees. Technician education programs and community colleges are expressing growing interest in developing data programs. What is missing are strategies and supports that can enable colleges to rapidly respond to these opportunities and scale-up their efforts to train the next generation of data workers in a sustained and timely manner. This panel will present EDC’s Data Pathway Development Process designed to support faculty in the creation and launch of new data programs at two-year institutions that are aligned to local employment opportunities.},
journal = {Proceedings of SITE Interactive 2020 Online Conference},
volume = {2020},
number = {1},
author = {Malyn-Smith, J. and MacGillivray, S. and Harris, M. and Polzin, J.},
editor = {Langren, E.}
}
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