Transforming Interests into STEM Careers (TISC)
The proposed study, Transforming Interests into STEM Careers (TISC), will test a model for promoting a STEM college-going culture in two high schools. The main goal of the intervention model is to encourage adolescents to pursue STEM majors in college and occupations in these fields. The study's focus is upon understanding the various factors that determine young students' interests in STEM disciplines and their entry into actual STEM careers. The study design includes four schools (two rural and two urban), all of which have lower than expected college going rates. One rural and one urban school will be the treatment group, the other two, the control group. The main purpose of the TISC intervention is to promote a school-wide college-going culture in which all students in a school are encouraged to perceive themselves as college applicants in the STEM fields, with their teachers, administrators, and parents sharing these expectations. The research study will undertake: (1) to implement the TISC intervention in two schools; and (2) to evaluate the effectiveness of the overall intervention, as well as each specific component (e.g., multi-tiered mentoring, course planning and sequencing, creating a school-wide college-going culture, financial aid planning, exposure to STEM careers, and teacher professional development in mathematics and science classes). The key research questions are: (1) to what extent is TISC effective at increasing engagement with STEM fields at the high school level? Which components of TISC are most effective at increasing engagement? (2) How effective is TISC at increasing entry into STEM postsecondary fields? (3) How effective is TISC in increasing persistence in STEM fields? Data will be collected using five instruments: (1) Teenage Life Questionnaire; (2) Career Orientation Survey; (3) "College Culture" interviews; 4) Experience Sampling Method on the subjective perceptions; and (5) Young Adult Follow-Up Survey. These data will be analyzed using a variety of quantitative data analysis methods, including regression models and Hierarchical Linear Models to answer the research questions.