Do Alternative College Options Benefit Non-Traditional Students, and How Do They Learn about Them?

This mixed-methods study aims to identify steps low-income and low-test high school students can take that are more strongly associated with credential and career success. These youth face added obstacles but may not see alternative options that avoid those obstacles, and perhaps even lead to the same goals. We also seek to understand how high school guidance counselors advise students and what information might help them. Quantitative Methods Analyzing the Education Longitudinal Study (ELS:2002-2012), we will describe how students’ non-cognitive behaviors, occupational courses (CTE), credentials, and objective job attributes (O*NET) are associated with various career outcomes, especially for low-test and low-SES students. For instance, does low-SES prevent students with high achievement, positive non-cognitive behaviors, and CTE courses from having decent odds of succeeding at any credential, getting good earnings, or getting desirable jobs? Can higher levels of non-cognitive behaviors and CTE course-taking offset the negative effects of low-SES or low-achievement for some credentials? These analyses can suggest specific advice to students about more attainable alternative pathways to various kinds of success. Qualitative Methods Given recent radical changes, it is crucial that high school counselors understand the new post-high school options and advise students, particularly low-income and low-test students. High school counselors are often important sources of career advice, especially for low-SES students whose parents may know little about college or good jobs. Counselors may be aware of these issues or vague about the new college reality of well-paying sub-BA credentials and how they should advise non-traditional students. In a small study, we will interview 20 counselors in Chicago and 5 in a suburb to learn how they think about various credentials and careers, what college advice they give to different students, and how that advice fits the realities we discover in our quantitative analyses. We also examine how college-advising software (e.g., Naviance) contributes to their advising.