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The STEM Excellence through Engagement in Collaboration, Research, and Scholarship (SEECRS) project at Whatcom Community College is in year four of a five-year NSF S-STEM funded program aiming to support academically talented students with demonstrated financial need in biology, chemistry, geology, computer science, engineering, and physics. This program offered financial, academic, and professional support to three two-year cohorts of students and is in the final year of the third and final cohort of the currently funded grant cycle. The SEECRS project aimed to utilize a STEM-specific guided pathways approach to strengthen recruitment, retention, and matriculation of STEM students at the community college level. Over the course of the program 39 individuals received scholarship support. The program supported scholarship recipients through participation in the SEECRS Scholars Academy, a multi-pronged approach to student support combining elements of community building, faculty mentorship, targeted advising activities, authentic science practice, and social activities. Key elements of the program are: a required two-credit course that emphasized STEM identity development, course-based undergraduate research experiences (CUREs) in Biology, Chemistry and Engineering courses, funded summer research opportunities, and paring of each scholar with a faculty mentor. This paper presents data from the first four years of the program including participant outcomes and feedback on their experiences. Results from project evaluation activities such as pre and post surveys, focus groups, exit interviews, and faculty surveys are also presented and analyzed to compare how gains reported by program participants regarding such attributes as their STEM identities and sense of belonging compare to responses from a control group of students who did not participate in the program. Preliminary identification of some program best practices will also be presented.
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Crowdsourcing Classroom Observations to Identify Misconceptions in Data Science
Web-browsing histories, online newspapers, streaming music, and stock prices all show that we live in an age of data. Extracting meaning from data is necessary in many fields to comprehend the information flow. This need has fueled rapid growth in data science education aiming to serve the next generation of policy makers, data science researchers, and global citizens. Initially, teaching practices have been drawn from data science's parent disciplines (e.g., computer science and mathematics). This project addresses the early stages of developing a concept inventory of student difficulty within the newly emerging field of data science. In particular this project will address three primary research objectives: (1) identify student misconceptions in data science courses; (2) document students’ prior knowledge and identify courses that teach early data science concepts; and (3) confirm expert identification of data science concepts, and their importance for introductory-level data science curricula. During the first year of this grant, we have collected approximately 200 responses for a survey to confirm concepts from an existing body of knowledge presented by the Edison Project. Survey respondents are comprised of faculty and industry practitioners within data science and closely related fields. Preliminary analysis of these results will be presented with respect to our third research objective. In addition, we developed and launched a pilot assessment for identifying student difficulties within data science courses. The protocol includes regular responses to reflective questions by faculty, teaching assistants, and students from selected data science courses offered at the three participating institutions. Preliminary analyses will be presented along with implications for future data collection in year two of the project. In addition to the anticipated results, we expect that the data collection and analysis methodologies will be of interest to many scholars who have or will engage in discipline-based educational research.
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- PAR ID:
- 10191570
- Date Published:
- Journal Name:
- 2020 ASEE Virtual Annual Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18260/1-2--34359
