Letters of recommendation in Berkeley undergraduate admissions: Program evaluation and natural language processing

In Fall 2015 and 2016, UC Berkeley asked many freshman applicants to submit letters of recommendation as part of their applications. This was highly controversial. Proponents argued that letters would aid in the identification of disadvantaged students who had overcome obstacles that were not otherwise apparent from their applications, while opponents argued that disadvantaged students were unlikely to have access to adults who could write strong letters. I oversaw an experiment in the 2016-17 admissions cycle in which applications were scored with and without their letters. Initial analysis of the experiment indicated that when available the letters modestly improved the reader scores of students from underrepresented groups, and that few otherwise admissible students failed to submit letters when asked. I will also present results of a textual analysis of the letters themselves, using natural language processing to measure differences in the letters that underrepresented students receive compared to otherwise similarly qualified students not from underrepresented groups.

The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features Berkeley faculty doing visionary research that illustrates the character of the ongoing data revolution.  This lecture series is offered to engage our diverse campus community and enrich active connections among colleagues.  All campus community members are welcome and encouraged to attend. Arrive at 3:30 PM for light refreshments and discussion prior to the formal presentation.