Learning from Students' Questions about the College Application Process
Lily Fesler

About the research


NAEd/Spencer Dissertation Fellowship

Award Year



Stanford University

Primary Discipline

I use text-as-data techniques to study the ways in which remote college counselors can successfully nudge forward low- and middle-income students during the college application process. Using two-way text messaging data between 15,000 high school seniors and their remote college counselors, I measure if and how students make progress in their college application during each of their interactions with their counselors. In particular, I study whether students make progress in terms of constructing their college list, submitting their college applications, filling out their financial aid applications, finding and understanding their financial aid offers, and completing any necessary summer steps (like signing up for orientation and taking any necessary placement tests) within each interaction. I consider students to have been successfully nudged forward if they considered changing their college list, learned new information about how to navigate the college application process (e.g. had a question answered about their financial aid offer), or were reminded to take a step that they had not yet taken (e.g. were reminded to finish their incomplete FAFSA). I manually code a subset of interactions, then use supervised machine learning techniques to predict the codes for the remaining interactions. In addition to measuring whether remote counselors can help students progress in some parts of their college applications more frequently than others, I also measure whether these frequencies vary by student and high school characteristics.
About Lily Fesler
Lily Fesler is a doctoral candidate in economics of education at the Stanford Graduate School of Education. Her research focuses on inequality in access to higher education, and how students' postsecondary choices are influenced by information about the college application process, personal preferences, and various programs and policies. She uses text-as-data and machine learning techniques to characterize students' experiences navigating the college application process, and quasi-experimental techniques to identify the causal effects of postsecondary access programs. She has served as a teaching assistant for courses on applied quasi-experimental methods in education and founded the student group Computational Text Analysis in the Social Sciences at Stanford. She is an Institute of Education Sciences (IES) Fellow and a Stanford Graduate Fellow, and she received her MA in economics at Stanford and her BA in economics from Wesleyan University.

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