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1. Approaches for Efficiently Identifying and Characterizing Student Need Assessments in Two-Year Colleges

   https://doi.org/10.18260/1-2--55452  

   Krupczak, John; Brown, David; Chan_Hilton, Amy (June 2025, ASEE Conferences)

   This paper describes an approach that can be used by faculty and administrators to efficiently develop program-level student support plans to increase retention and completion in STEM disciplines. These recommendations were developed as part of a National Science Foundation-sponsored workshop intended to help two-year college faculty and administrators prepare proposals for the National Science Foundation Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) Program. S-STEM scholarship proposals are expected to be built on a foundation of deep needs analyses specific to the targeted population of students in STEM disciplines. Based on needs assessment, programs can then focus on implementing appropriate interventions that will be most effective in improving the retention and completion of their students. Guidelines for streamlining the acquisition and organization of critical elements of student needs analyses can be useful for two-year college faculty and administrators to develop NSF S-STEM proposals and any other initiatives they may pursue to improve student success at their institutions. Our approach recognizes that needs analysis benefits from three levels of data: institutional data, program-level data, and student-level data. Institutional-level data includes retention and completion data as well as results of institutional-level surveys of current students or alumni and the National Survey of Student Engagement (NSSE). Program-level data includes retention and completion data at the program level that may show significant differences from institutional results. In addition, program data should include course-level grades and failure rates, student GPA correlated with student program year, and student demographic data if available. The program data can help identify attrition points at the program level. Student data forms a third level that can clarify and focus student needs analyses. One aspect of student-level data is personal attributes associated with academic and career success in STEM fields. Examples include a growth mindset, stem identity, a sense of belonging, and academic self-efficacy. The validated surveys that exist to characterize these attributes are outlined in the paper. These surveys can be used at the program level to identify both baseline data and critical needs. In parallel with surveys, the creation of a student need archetype using techniques from the NSF I-Corps for Learning (I-Corps L) model can be used to elicit another dimension of challenges faced by students. The I-Corps for Learning model emphasizes the benefit of unstructured one-on-one informal interviews to elicit unscripted data from students to test assumptions and uncover opportunities for impact. The paper provides step-by-step guidelines for efficient implementation of I-Corps for Learning student needs discovery methods. In summary, even with external grant funding such as NSF S-STEM funds, student support initiatives must allocate available funds strategically to obtain the most impact. Collection of data at institutional, program, and student levels can facilitate the synthesis of a student-need archetype that supports faculty and administrative decision-makers. This paper aims to provide practical guidelines to two-year college faculty and administrators for creating a compelling student needs assessment and characterization of institutional context. 

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2. EAGER: Student Support in STEM: Developing and Validating a Survey Instrument for Assessing the Magnitude of Institutional Support Provided to Undergraduate Students at a College Level

   Lee, W.C.; Knight, D.B.; Godwin, A.; Moyer, L.A.; Hasbun, I.M. (June 2018, ASEE annual conference & exposition)

   Student-retention theories traditionally focus on institutional retention, even though efforts to support students in science, technology, engineering, and mathematics (STEM) occur at the college level. This study bridges this gap between research and practice by extending and empirically testing the Model of Co-Curricular Support (MCCS), which specifically focuses on supporting and retaining underrepresented groups in STEM. The MCCS is a student-retention model that demonstrates the breadth of assistance currently used to support undergraduate students in STEM, particularly those from underrepresented groups. The aim of this exploratory research is to develop and validate a survey instrument grounded in the MCCS that can be used by college administrators and student-support practitioners to assess the magnitude of institutional support received by undergraduate students in STEM. To date, such an instrument does not exist. Our poster will present a summary of the instrument development process that has occurred to date. We are developing the survey following best practices outlined in the literature. We are clearly defining the construct of interest and target population; reviewing related tests; developing the prototype of the survey instrument; evaluating the prototype for face and content validity from students and experts; revising and testing based on suggestion; and collecting data to determine test validity and reliability across four institutional contexts. Our institutional sample sites were purposefully selected because of their large size and diversity with respect to undergraduates in STEM. The results from our study will help prioritize the elements of institutional support that should appear somewhere in a college’s suite of support efforts. Our study will provide scientific evidence that STEM researchers, educators, administrators, and policy makers need to make informed decisions to improve STEM learning environments and design effective programs, activities, and services. 

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3. Investing in Instructors: Creating Intelligent Feedback Loops in Large Foundational Courses for Undergraduate Engineeringg

   Grohs, J.R.; Knight, D.B.; Soledad, M.M.; Case, S.W.; Murzi, H.G.; Smith, N. (June 2018, ASEE Annual Conference proceedings)

   The drive to encourage young people to pursue degrees and careers in engineering has led to an increase in student populations in engineering programs. For some institutions, such as large public research institutions, this has led to large class sizes for courses that are commonly taken across multiple programs. While this decision is reasonable from an operational and resource management perspective, research on large classes have shown that students suffer decreased engagement, motivation and achievement. Instructors, on the other hand, report having difficulty establishing rapport with their students and a growing inability to monitor students’ learning gains and provide quality individualized feedback. To address these issues, our project draws from Lattuca and Stark’s Academic Plan model, which incorporates a thorough consideration of factors influencing curricular activities that can be applied at the course, program, and institutional levels, and assumes that instructors are key actors in curriculum development and revision. We aim to revitalize feedback loops to help instructors and departments continuously improve. Recognizing that we must understand both individual and systems level perspectives, we prioritize regular engagement between faculty and institutional support structures to collaboratively identify problems and systematically establish continuous improvement. In the first phase of this NSF IUSE Institutional Transformation project, we focus on specifically prompting and studying the experiences of 8 instructors of foundational engineering courses usually taught in large class sizes across 4 different departments at a large public research institution. We collected qualitative data (semi-structured interviews, reflective journals, course-related documents) and quantitative data (student surveys and institution-provided transcript data) to answer research questions (e.g., what data do faculty teaching large foundational undergraduate engineering courses identify as being useful so that they may enhance students’ experiences and outcomes within the classes that they teach and across students’ multiple large classes?) at the intersection of learning analytics and faculty change. The data was used as a baseline to further refine data collection protocols, identify data that faculty consider meaningful and useful for managing large foundational engineering courses, and consider ways of productively leveraging institutional data to improve the learning experience in these courses. Data collection for the first phase is ongoing and will continue through the Spring 2018 semester. Findings for this paper will include high-level insights from Fall interviews with instructors as well as data visualizations created from the population-level data characterizing student performance in the foundational courses within the context of pre-college characteristics (e.g., SAT scores) and/or other academic outcomes (e.g., major switching within or out of engineer, degree attainment). 

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4. Leveraging BIPOC Faculty Counterspaces: Lessons for Organizational Change from Aspire’s IThrive Collective

   Thomas, S; Peeples, T; Bennett, J (September 2023, Journal of STEM Education: Innovations and Research)

   This exploratory study examines the relationship between Aspire’s IThrive Collective counterspace community of support and the organizational transformation efforts of members of the IChange Network. Our study examines how a counterspace community of support could inform institutional transformation. We collected focus group data from participants in a IThrive counterspace conversation series, consisting of five gatherings from 2021-2022. Using Griffin’s (2020) institutional model for faculty diversity, we developed a codebook to capture areas of activity desired by faculty and university action plans. Preliminary results show an emerging framework to disaggregate impressions of faculty from dominant and underrepresented groups to inform transformation. 

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5. Challenges in large-scale bioinformatics projects

   https://doi.org/10.1057/s41599-022-01141-4  

   Morrison-Smith, Sarah; Boucher, Christina; Sarcevic, Aleksandra; Noyes, Noelle; O’Brien, Catherine; Cuadros, Nazaret; Ruiz, Jaime (December 2022, Humanities and Social Sciences Communications)

   Abstract Biological and biomedical research is increasingly conducted in large, interdisciplinary collaborations to address problems with significant societal impact, such as reducing antibiotic resistance, identifying disease sub-types, and identifying genes that control for drought tolerance in plants. Many of these projects are data driven and involve the collection and analysis of biological data at a large-scale. As a result, life-science projects, which are frequently diverse, large and geographically dispersed, have created unique challenges for collaboration and training. We examine the communication and collaboration challenges in multidisciplinary research through an interview study with 20 life-science researchers. Our results show that both the inclusion of multiple disciplines and differences in work culture influence collaboration in life science. Using these results, we discuss opportunities and implications for designing solutions to better support collaborative tasks and workflows of life scientists. In particular, we show that life science research is increasingly conducted in large, multi-institutional collaborations, and these large groups rely on “mutual respect” and collaboration. However, we found that the interdisciplinary nature of these projects cause technical language barriers and differences in methodology affect trust. We use these findings to guide our recommendations for technology to support life science. We also present recommendations for life science research training programs and note the necessity for incorporating training in project management, multiple language, and discipline culture. 

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