2025 ASEE Annual Conference & Exposition

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.