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A College Major Recommendation System

Published: 22 September 2020 Publication History

Abstract

College students are required to select a major but are often provided with only a modest amount of support in making this important decision. A poor decision is detrimental to the student, since it may result in the student later switching to a different major with a delay in graduation—or even result in the student leaving the university. This also impacts the university since time to graduation and retention rate are used to evaluate the quality of a university. There is a general lack of research on recommender systems for college majors, with the most relevant systems focusing on course-level recommendations. This study describes and evaluates a recommender system for selecting an undergraduate major, utilizing nine years of historical student data from a large university. The system bases its recommendations on the courses that the student takes in the first few years of college, and how well they performed in these courses. The system is designed to recommend majors that the student is likely to be interested in and will perform well in. Recommendations are evaluated based on the likelihood that the student's actual major was in the top five recommended majors, and whether the student performed above average in that major. The recommendation system dramatically outperforms the baseline strategy of randomly selecting a major, and when the recommendation is followed the student is 12% more likely to perform above average in the major.

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Cited By

View all
  • (2024)Dissecting bias of ChatGPT in college major recommendationsInformation Technology and Management10.1007/s10799-024-00430-5Online publication date: 25-Jun-2024
  • (2023)When Biased Humans Meet Debiased AI: A Case Study in College Major RecommendationACM Transactions on Interactive Intelligent Systems10.1145/361131313:3(1-28)Online publication date: 11-Sep-2023
  • (2023)A Novel Content Over Context Based Filtering for College Recommendation System and Enhanced Accuracy Rate Enabled Hybrid Framework2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)10.1109/ICACITE57410.2023.10182430(1475-1478)Online publication date: 12-May-2023
  • Show More Cited By

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Published In

cover image ACM Conferences
RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems
September 2020
796 pages
ISBN:9781450375832
DOI:10.1145/3383313
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 September 2020

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Author Tags

  1. Recommender systems
  2. collaborative filtering
  3. educational data mining
  4. nearest neighbor

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  • Extended-abstract
  • Research
  • Refereed limited

Conference

RecSys '20: Fourteenth ACM Conference on Recommender Systems
September 22 - 26, 2020
Virtual Event, Brazil

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Upcoming Conference

RecSys '24
18th ACM Conference on Recommender Systems
October 14 - 18, 2024
Bari , Italy

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Cited By

View all
  • (2024)Dissecting bias of ChatGPT in college major recommendationsInformation Technology and Management10.1007/s10799-024-00430-5Online publication date: 25-Jun-2024
  • (2023)When Biased Humans Meet Debiased AI: A Case Study in College Major RecommendationACM Transactions on Interactive Intelligent Systems10.1145/361131313:3(1-28)Online publication date: 11-Sep-2023
  • (2023)A Novel Content Over Context Based Filtering for College Recommendation System and Enhanced Accuracy Rate Enabled Hybrid Framework2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)10.1109/ICACITE57410.2023.10182430(1475-1478)Online publication date: 12-May-2023
  • (2022)A Recommendation System for Selecting the Appropriate Undergraduate Program at Higher Education Institutions Using Graduate Student DataApplied Sciences10.3390/app12241252512:24(12525)Online publication date: 7-Dec-2022
  • (2022)Improved Precision Rate in a Hybrid based Framework for College Recommendation System using Novel Content based Filtering over Keyword Map Algorithm2022 3rd International Conference on Smart Electronics and Communication (ICOSEC)10.1109/ICOSEC54921.2022.9952145(1581-1584)Online publication date: 20-Oct-2022
  • (2021)Selection of the Right Undergraduate Major by Students Using Supervised Learning TechniquesApplied Sciences10.3390/app11221063911:22(10639)Online publication date: 11-Nov-2021

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