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Classifying Courses at Scale: A Text as Data Approach to Characterizing Student Course-Taking Trends with Administrative Transcripts. EdWorkingPaper No. 24-1042

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Title: Classifying Courses at Scale: A Text as Data Approach to Characterizing Student Course-Taking Trends with Administrative Transcripts. EdWorkingPaper No. 24-1042
Language: English
Authors: Annaliese Paulson, Kevin Stange, Allyson Flaster, Annenberg Institute for School Reform at Brown University
Source: Annenberg Institute for School Reform at Brown University. 2024.
Availability: Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: AISR_Info@brown.edu; Web site: http://www.annenberginstitute.org
Peer Reviewed: N
Page Count: 64
Publication Date: 2024
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305B200011
Document Type: Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Undergraduate Students, Course Selection (Students), Majors (Students), Sample Size, Academic Records, Classification, Liberal Arts, Professional Education, Educational Trends, Educational Indicators, Educational Change
Abstract: Students' postsecondary course-taking is of interest to researchers, yet has been difficult to study at large scale because administrative transcript data are rarely standardized across institutions or state systems. This paper uses machine learning and natural language processing to standardize college transcripts at scale. We demonstrate the approach's utility by showing how the disciplinary orientation of students' courses and majors align and diverge at 18 diverse four-year institutions in the College and Beyond II dataset. Our findings complicate narratives that student participation in the liberal arts is in great decline. Both professional and liberal arts majors enroll in a large amount of liberal arts coursework, and in three of the four core liberal arts disciplines, the share of course-taking in those fields is meaningfully higher than the share of majors in those fields. To advance the study of student postsecondary pathways, we release the classification models for public use. [Additional funding provided by the Michigan Institute of Data Science at the University of Michigan.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2024
Accession Number: ED661566
Database: ERIC
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  Data: Students' postsecondary course-taking is of interest to researchers, yet has been difficult to study at large scale because administrative transcript data are rarely standardized across institutions or state systems. This paper uses machine learning and natural language processing to standardize college transcripts at scale. We demonstrate the approach's utility by showing how the disciplinary orientation of students' courses and majors align and diverge at 18 diverse four-year institutions in the College and Beyond II dataset. Our findings complicate narratives that student participation in the liberal arts is in great decline. Both professional and liberal arts majors enroll in a large amount of liberal arts coursework, and in three of the four core liberal arts disciplines, the share of course-taking in those fields is meaningfully higher than the share of majors in those fields. To advance the study of student postsecondary pathways, we release the classification models for public use. [Additional funding provided by the Michigan Institute of Data Science at the University of Michigan.]
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