Vinetta C. Jones, Ph.D. Howard University QEM—Baltimore, Maryland September 20, 2013

1. Broadening Research Participation in STEMbyBiology, Chemistry, Computer Science, Engineering, Mathematics, Physics, Education and Social Science faculty TeamsNational Science FoundationHBCU-UP, CREST and TCUP Programs Vinetta C. Jones, Ph.D. Howard University QEM—Baltimore, Maryland September 20, 2013

2. Broadening STEM Participation of African American Males The Problem Small and dwindling numbers of African American Males entering college with the preparation and interest to pursue a STEM career. In 2008 African American males made up 15.4% of the 15-24 age population (US Census Bureau, 2009). However, they received only 5.0% of the STEM bachelor's degrees awarded (NCES, 2009). The nation's economic prosperity is dependent upon a backbone of diverse groups of individuals knowledgeable about and able to work in quantitative and technical fields. Thus the diverse demographic make-up of the US requires increasing the presence of underrepresented groups in the quantitative and technical fields.

3. The Problem Absence of a comprehensivetheory that identifies the factors that are necessary to promote excellence in mathematics and science among underrepresented groups. Isolating the factors that facilitate or obstruct Black males' preparation for and interest in STEM college majors is hindered by the absence of analyses of gender within race/ethnic group. This prevents a specific focus on African American males.

4. Intellectual Merit Disentangling the issues of race and gender and their relationship to factors influencing STEM interest and participation will advance knowledge within education and across STEM fields. The theory-based research design and psychometrically sound survey instrument produced during the project using the HSLS:2009 NCES database should point to ways to refine STEM interventions for underrepresented groups.

5. Conceptual Framework: Engagement, Capacity and Continuity Trilogy Theory(Jolly, Campbell, and Perlman. 2004) Factors leading to achievement in math and science Student Guiding Functions, Asset-Focused Strategies, Student Engagement (Boykin, Noguera, 2011) Factors leading to Achievement (gap-closing) of African American students

6. Four Factor Hybrid Model(ECC Trilogy Model & Boykin/Noguera model) Engagement – (Student level) Interests and attraction to math or science – eagerness to learn, persistence. Includes cognitive, behavioral, affective & vocational components. Capacity – Acquired knowledge & skills to advance to rigorous quantitative content (e.g., level of math course in 9th grade). Continuity – Institutional & programmatic opportunities, resources and guidance that support advancement to rigorous content in science and other quantitative disciplines, (e.g., teacher practices, what the school is doing). Guiding Functions – (Student beliefs) Student’s adaptive learning postures such as self-efficacy, self-regulated learning and incremental ability beliefs.

7. Research Questions Phase 1 Is there a significant race by gender interaction effect on mathematics achievementusing the HSLS:09 database? Phase 2 Are the factors in the Four Factor Hybrid Model (ECC Trilogy Model & Boykin/Noguera model) positively associated with math achievement for 9th graders and are they predictive of STEM achievement for undergraduate Black males?

8. Initial Findings • There is an overall main effect of gender on math achievement in the HSLS:09 • There is a small nonsignificant interaction between race (black vs. white) and gender on math achievement in the HSLS:09

9. Product For researchers, the theory-based, replicable research design and the psychometrically sound survey instrument produced during the project will make possible more efficient investigation of underrepresented subgroups.

10. Challenges • Insufficient support for a graduate assistant • A supplemental grant for a graduate assistant was requested and awarded. • IRB approval was required when grant was initially submitted rather than when awarded. • Delays in access to supplemental grant funds • Data analysis required restricted use HSLS:09 database for SPSS. We used STATA software which limited access by research team. • Difficulty matching items from HSLS:09 questionnaire to the four factors of the Hybrid Model. Some of the factors had fewer items available which were appropriate.