An official website of the United States government
Abstract Programs that emphasize the important, multifaceted roles that soils play as the interfaces between humanity and the earth are needed to attract new students from diverse backgrounds. The Basic and Environmental Soil Science Training Research Experience for Undergraduates (BESST REU) site aims to provide undergraduates from a variety of science, technology, engineering, and mathematics disciplines with a substantive research experience on modern soil science while developing professional and technical skills. Here, we examine data from the first five BESST cohorts pre‐ and post‐activity surveys, assessing effects of the experience on students’ research skills, knowledge about soil science, and science self‐efficacy. Results suggest that both program‐wide, common experiences, such as our geomorphology field camp, and specific, individual, mentored research experiences were important to student outcomes. Students experienced significant increases in their capacity to conduct research, knowledge about soil science, and confidence in carrying out scientific tasks. Further, qualitative data suggest that the program fundamentally impacted how students saw themselves and understood the task of “doing science.” Experiences provided by REU programs such as BESST are valuable to help students refine their personal goals and understanding of communication and collaboration within research. BESST also helped students to better understand and communicate the role of soil scientists.
more »
« less
Curating datasets to support middle school student inquiry
We examine how developers of data science curricula determine what makes a pedagogically effective dataset enabling 10–14 year-old students (“middle school” in the United States) to engage in the data investigation cycle by posing their own questions about relationships among variables. We describe strategies for curating existing datasets to address goals for learning about data, and for optimizing the use of these datasets once they are curated. We investigate how data science educators can transform existing datasets into ones appropriate for students with little data experience, drawing on our experience working with several publicly available datasets, which students explored in CODAP (the Common Online Data Analysis Platform).
more »
« less
- Award ID(s):
- 2313212
- PAR ID:
- 10638703
- Publisher / Repository:
- International Association for Statistics Education
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A central goal of the Learning Assistant (LA) model is to improve students’ learning of science through the transformation of instructor practices. There is minimal existing research on the impact of college physics instructor experiences on their effectiveness. To investigate the association between college introductory physics instructors’ experiences with and without LAs and student learning, we drew on data from the Learning About STEM Student Outcomes (LASSO) database. The LASSO database provided us with student-level data (concept inventory scores and demographic data) for 4,365 students and course-level data (instructor experience and course features) for the students’ 93 mechanics courses. We performed Hierarchical Multiple Imputation to impute missing data and Hierarchical Linear Modeling to nest students within courses when modeling the associations be- tween instructor experience and student learning. Our models predict that instructors’ effectiveness decreases as they gain experience teaching without LAs. However, LA supported environments appear to remediate this decline in effectiveness as instructor effectiveness is maintained while they gain experience teaching with LAs.more » « less
-
Frank, Brian W.; Jones, Dyan L.; Ryan, Qing X. (Ed.)The ways in which physics majors make career decisions is a critical, yet understudied, aspect of the undergraduate experience. Such decisions are important to students, physics departments, and administrators. In this project, we specifically examine how students develop interests and intent to pursue specific subfields of physics by interviewing 13 physics majors from all years of study. The interviews examined factors that led students to choose their most preferred and least preferred subfields. Interviews leveraged the framework of Social Cognitive Career Theory, a model that describes how several constructs such as self-efficacy, learning experiences, and outcome expectations relate to decision-making. Findings highlight the differences in decision-making between upper-division students and beginning students. For instance, we see how popular culture and popular science provide an initial learning experience about certain subfields, such as astronomy and astrophysics, which strongly affect beginning students' perceptions of that subfield. Initial exposure to biology and chemistry in high school or early undergraduate classes often negatively affected students' interests in fields like biophysics or chemical physics. Data also suggests a splitting between students with respect to their outcome expectations of a desirable career in science. While some students prioritize using science to help people, others prioritize discovery of new knowledge though science, and some are in between. Students in both groups form perceptions about subfields that do not align with their identities and hence make decisions based on these perceptions. For instance, a student who prioritizes helping others through science may be quick to reject astrophysics as a subfield choice as they do not think that astrophysics can help people enough.more » « less
-
As educators and researchers, we often enjoy enlivening classroom discussions by including examples of cutting-edge high-throughput (HT) technologies that propelled scientific discovery and created repositories of new information. We also call for the use of evidence-based teaching practices to engage students in ways that promote equity and learning. The complex datasets produced by HT approaches can open the doors to discovery of novel genes, drugs, and regulatory networks, so students need experience with the effective design, implementation, and analysis of HT research. Nevertheless, we miss opportunities to contextualize, define, and explain the potential and limitations of HT methods. One evidence-based approach is to engage students in realistic HT case studies. HT cases immerse students with messy data, asking them to critically consider data analysis, experimental design, ethical implications, and HT technologies.The NSF HITS (High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students) Research Coordination Network in Undergraduate Biology Education seeks to improve student quantitative skills and participation in HT discovery. Researchers and instructors in the network learn about case pedagogy, HT technologies, publicly available datasets, and computational tools. Leveraging this training and interdisciplinary teamwork, HITS participants then create and implement HT cases. Our initial case collection has been used in >15 different courses at a variety of institutions engaging >600 students in HT discovery. We share here our rationale for engaging students in HT science, our HT cases, and network model to encourage other life science educators to join us and further develop and integrate HT complex datasets into curricula.more » « less
-
Abstract Today, due to the size of many genomes and the increasingly large sizes of sequencing files, independently analyzing sequencing data is largely impossible for a biologist with little to no programming expertise. As such, biologists are typically faced with the dilemma of either having to spend a significant amount of time and effort to learn how to program themselves or having to identify (and rely on) an available computer scientist to analyze large sequence data sets. That said, the advent of AI‐powered programs like ChatGPT may offer a means of circumventing the disconnect between biologists and their analysis of genomic data critically important to their field. The work detailed herein demonstrates how implementing ChatGPT into an existing Course‐based Undergraduate Research Experience curriculum can provide a means for equipping biology students with no programming expertise the power to generate their own programs and allow those students to carry out a publishable, comprehensive analysis of real‐world Next Generation Sequencing (NGS) datasets. Relying solely on the students' biology background as a prompt for directing ChatGPT to generate Python codes, we found students could readily generate programs able to deal with and analyze NGS datasets greater than 10 gigabytes. In summary, we believe that integrating ChatGPT into education can help bridge a critical gap between biology and computer science and may prove similarly beneficial in other disciplines. Additionally, ChatGPT can provide biological researchers with powerful new tools capable of mediating NGS dataset analysis to help accelerate major new advances in the field.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.52041/iase2023.106
