A Quantitative Machine Learning Approach to Evaluating Letters of Recommendation

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dc.contributor.author Zhao, Yijun
dc.contributor.author Wang, Tianyu
dc.contributor.author Mensah, Douglas
dc.contributor.author Parnoff, Ellise
dc.contributor.author He , Siyi
dc.contributor.author Weiss, Gary
dc.date.accessioned 2023-12-26T18:36:56Z
dc.date.available 2023-12-26T18:36:56Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other fe67f4f1-627f-424c-a67c-f07090762196
dc.identifier.uri https://hdl.handle.net/10125/106534
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Educational Data Mining for Decision Making
dc.subject graduate admissions
dc.subject letters of recommendation
dc.subject machine learning
dc.subject natural language processing
dc.title A Quantitative Machine Learning Approach to Evaluating Letters of Recommendation
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Letters of Recommendation (LOR) are key components of the undergraduate and graduate admissions process. A fair and objective evaluation of these LORs is difficult due to diverse applicant-recommender relationships, a lack of standardized criteria, and limited resources for reviewing the LORs. In this paper, we describe three criteria, relevance, specificity, and positivity, for characterizing the quality of an LOR. Approximately 4,000 LORs written in support of students applying to either a Master's in Computer Science or a Master's in Data Science degree are manually rated using these criteria along with rating guidelines developed for this study. Predictive models utilizing natural language processing and machine learning are trained to predict these ratings directly from the LOR text. The work described in this paper can aid in objective and automatic assessment of LORs, or help the admissions committee selectively review the LORs when resources are limited. This work can be extended to support the admissions process for other graduate and undergraduate programs.
dcterms.extent 9 pages
prism.startingpage 1276
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