A Quantitative Machine Learning Approach to Evaluating Letters of Recommendation

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dc.contributor.authorZhao, Yijun
dc.contributor.authorWang, Tianyu
dc.contributor.authorMensah, Douglas
dc.contributor.authorParnoff, Ellise
dc.contributor.authorHe , Siyi
dc.contributor.authorWeiss, Gary
dc.date.accessioned2023-12-26T18:36:56Z
dc.date.available2023-12-26T18:36:56Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.157
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherfe67f4f1-627f-424c-a67c-f07090762196
dc.identifier.urihttps://hdl.handle.net/10125/106534
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEducational Data Mining for Decision Making
dc.subjectgraduate admissions
dc.subjectletters of recommendation
dc.subjectmachine learning
dc.subjectnatural language processing
dc.titleA Quantitative Machine Learning Approach to Evaluating Letters of Recommendation
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractLetters 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.extent9 pages
prism.startingpage1276

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