Forecasting Undergraduate Majors Using Academic Transcript Data
Author(s) -
David Lang,
Alex Wang,
Nathan Dalal,
Andreas Paepcke,
Mitchell L. Stevens
Publication year - 2021
Publication title -
ed (osf preprints)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/3430895.3460149
Subject(s) - voting , computer science , mathematics education , class (philosophy) , terminal (telecommunication) , scale (ratio) , course (navigation) , undergraduate education , artificial intelligence , psychology , medical education , engineering , political science , medicine , telecommunications , physics , quantum mechanics , politics , law , aerospace engineering
Committing to a major is a fateful step in an undergraduate's education, yet the relationship between courses taken early in an academic career and ultimate major choice remains little studied at scale. We analyze transcript data capturing the academic careers of 26,892 undergraduates at a private university between 2000 and 2020. We forecast students' terminal major on the basis of course-choice sequences beginning at university entry. We represent course enrollment history using natural-language methods and vector embeddings. We find that a student's very first enrolled course predicts their terminal major thirty times better than random guessing and more than a third better than majority class voting.
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