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Through an NSF S-STEM grant and institutional support, the STEM CAREERS (Career and Research Exploration to Enhance Retention in STEM) Program was created in 2018. This program provided scholarships and programming for high achieving lower-income students from rural and diverse backgrounds for N = 33 students over four years. A summer bridge, common first-year experience course, and interim trip on career exploration created a learning community for the cohort in their first year. Career exploration and networking opportunities introduced students to unimagined careers. Career preparation experiences were built into the remaining 3 years of the program as well as multi-layered mentoring. Mixed methods data collection included pre-post STEM career surveys, annual focus groups, and personal reflections. Some preliminary results include enhanced sense of belonging and strong support network, high placement into prestigious summer research programs and internships, and early admittance into dental school and graduate school.
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Improving Biostatistics Instruction through an Iterative Flipped Classroom Approach
Preparing students for STEM careers requires educational approaches that bridge conceptual understanding with practical skill development. This is particularly critical in biostatistics, a foundational course for students pursuing careers in research and data-driven fields. Traditional instruction often emphasizes conceptual knowledge without providing sufficient opportunities for authentic, hands-on data analysis. Research-embedded flipped classroom (REFC) models offer a promising alternative by delivering conceptual content through pre-class videos and using in-class time for guided analysis of real-world data. This approach may enhance students’ statistical proficiency, analytical confidence, and perceptions of STEM careers. This study evaluated the iterative implementation of a REFC model across three cohorts of undergraduate students enrolled in biostatistics courses at two Historically Black Colleges and Universities (HBCUs). A total of 493 responses from 359 students were analyzed using a factorial design comparing traditional instruction (IAU) with three REFC iterations. Measures included a 20-item biostatistics quiz (ω = .75–.84), the 12-item Career Interest Questionnaire (CIQ; ω = .92–.96), and the 25-item STEM Semantics Survey (STEMSS; ω = .79–.92). Results revealed a significant interaction between instructional cohort and time of assessment for biostatistics knowledge, F(3, 427) = 7.15, p < .001, η_p² = .05. Only Cohort 3—characterized by consistent instructional strategies, conceptual review during class, and use of StatCrunch software—showed significant gains from pre- to post-test and outperformed both the IAU and earlier REFC cohorts. No significant changes were observed across cohorts on measures of STEM interest or attitudes. Findings suggest that iterative refinement of REFC strategies can meaningfully improve students’ biostatistics knowledge, though additional supports may be needed to influence career interests. Recommendations include structured in-class reviews, learner-aligned technology, and modest incentives for engagement. Further research is needed to explore how enhanced statistical skills can translate into long-term STEM career aspirations, particularly across diverse institutional contexts and earlier stages of education.
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- Award ID(s):
- 2117087
- PAR ID:
- 10675194
- Publisher / Repository:
- Hawaii International Conference on Education
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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