|Title:||Designing Crowdsourced Mentorship to Support Low-Income High School Students' College Enrollment|
|Principal Investigator:||Ali, Alisha||Awardee:||New York University|
|Program:||Researcher-Practitioner Partnerships in Education Research [Program Details]|
|Award Period:||2 years (09/01/2018–08/31/2020)||Award Amount:||$396,816|
|Type:||Researcher-Practitioner Partnership||Award Number:||R305H180051|
Co-Principal Investigator: Ahn, June and Homer, Bruce D.
Partner Institutions: New York City College Advising Corps (NY CAC), New York University (NYU), University of California-Irvine (UCI), and City University of New York (CUNY).
Purpose: This partnership will develop and test a mentorship model for extending advising to college-accepted high school graduates during the summer months after graduation. Prior research has documented a pattern of “summer melt” in which motivated and academically-prepared high school graduates who have been accepted to college decide not to enroll in college. Summer melt is especially prevalent among low-income, African American, and Latinx students. Through prior research, the partnership team identified a set of barriers that contribute to summer melt for New York City (NYC) high school students and concluded that combining the transmission of college knowledge with support for students' development of college-bound identity is a potential solution to this problem. This project will explore how to scale mentorship via a text-messaging application that incorporates social and emotional supports and coordinates personal interactions between students and a large team (or “crowd”) of mentors.
Partnership Activities: During year 1 of the project, the partnership will draw on experience and data from an initial text messaging campaign to iteratively design and improve its text messaging platform so that it connects students to mentors who can assist them with specific challenges they are facing during the summer after graduation. A key objective of the design process is development of an algorithm that can accurately classify the topics of students' texts, respond to messages, and connect students to mentors who possess the skills to assist them. The research team will pilot the crowdsourced text-messaging platform during summer 2019. During year 2, the team will analyze data from the pilot test and refine the platform accordingly. During summer 2020, the research team will test the refined platform through a randomized-controlled trial (RCT) of students from 15 high schools connected to a team of approximately 75 mentors.
Setting: This project will take place in New York City (NYC), one of the most diverse school districts in the United States. The New York City College Advising Corps (NY CAC) serves a student population where approximately 90% of the students are eligible for free/reduced lunch and come from underrepresented minority groups.
Population/Sample: The sample for this partnership project includes approximately 1,000 recent graduates from 15 NYC high schools who have received advising from NY CAC.
Data Analytic Strategy: Researchers will randomly assign half of the college-accepted graduates at each of the 15 high schools to receive the crowdsourcing application. The team will use logistic regression to assess treatment-control differences on key outcomes of interest and will also develop regression models to explore the relationship between specific mentorship experiences (via text), college-bound identity and knowledge, and enrollment decisions.
Outcomes: The partnership aims to significantly increase college enrollment among college-accepted students through its crowdsourced mentorship application and will generate preliminary evidence of the efficacy of this digital tool.