Book contents
- Higher Education Admissions Practices
- Educational and Psychological Testing in a Global Context
- Higher Education Admissions Practices
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Series Editor’s Foreword
- Foreword
- Acknowledgments
- Part I Global Challenges and Common Admissions Models
- Part II Country-Specific Admissions Practices
- Part III Assessments Used in Higher Education Admissions
- Part IV Rethinking Higher Education Admissions
- Chapter 17 The ACT Holistic Framework® of Education and Workplace Success
- Chapter 18 Using Mathematical Models to Improve Access to Postsecondary Education
- Chapter 19 After Admissions: What Comes Next in Higher Education?
- Index
- References
Chapter 18 - Using Mathematical Models to Improve Access to Postsecondary Education
from Part IV - Rethinking Higher Education Admissions
Published online by Cambridge University Press: 09 January 2020
Book contents
- Higher Education Admissions Practices
- Educational and Psychological Testing in a Global Context
- Higher Education Admissions Practices
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Series Editor’s Foreword
- Foreword
- Acknowledgments
- Part I Global Challenges and Common Admissions Models
- Part II Country-Specific Admissions Practices
- Part III Assessments Used in Higher Education Admissions
- Part IV Rethinking Higher Education Admissions
- Chapter 17 The ACT Holistic Framework® of Education and Workplace Success
- Chapter 18 Using Mathematical Models to Improve Access to Postsecondary Education
- Chapter 19 After Admissions: What Comes Next in Higher Education?
- Index
- References
Summary
This chapter proposes the use of a mathematical approach that helps support the access and diversity goals of higher education institutions while still maintaining academic standards. This approach, called constrained optimization, allows both academic requirements and other factors – race/ethnicity, income level, social status, geographic region, educational background – to be considered during the admissions process. While diversity efforts vary by country and institution, constrained optimization seeks to improve higher education access for particular groups of students. As such, this may be a useful approach for ensuring that the multiple objectives of the admissions process of any country are achieved.
Keywords
- Type
- Chapter
- Information
- Higher Education Admissions PracticesAn International Perspective, pp. 333 - 346Publisher: Cambridge University PressPrint publication year: 2020
References
Berkelaar, M. (2014). Package “lpSolve.” Retrieved from https://cran.r-project.org/web/packages/lpSolve/lpSolve.pdf.Google Scholar
Blatter, A., & Glynn, J. (2016, November). Recognizing distance traveled in selective college admissions. Presented at the annual meeting of the Association for the Study of Higher Education, Columbus, Ohio.Google Scholar
Cohen, D. (2004, February 13). In Malaysia, the end of quotas. Chronicle of Higher Education. Retrieved from www.chronicle.com/article/In-Malaysia-the-End-of-Quotas/10391.Google Scholar
Durán, G., & Wolf-Yadlin, R. (2011). A mathematical programming approach to applicant selection for a degree program based on affirmative action. Interfaces, 41, 278–288. https://doi.org/10.1287/inte.1100.0542.CrossRefGoogle Scholar
Ingels, S., Planty, M., & Bozick, R. (2005). A profile of the American high school senior in 2004: A first look-initial results from the first follow-up of the education longitudinal study of 2002 (NCES 2006-348). Washington, DC: US Department of Education, NCES.Google Scholar
Koljatic, M., & Silva, M. (2013). Opening a side-gate: Engaging the excluded in Chilean higher education through test-blind admission. Studies in Higher Education, 38, 1427–1441. https://doi.org/10.1080/03075079.2011.623299.Google Scholar
Kolman, B., & Beck, R. E. (1995). Elementary linear programming with applications (2nd ed.). San Diego, CA: Academic Press.Google Scholar
Kreiter, C. D. (2002). The use of constrained optimization to facilitate admission decisions. Academic Medicine, 77, 148–151. https://doi.org/10.1097/00001888-200202000-00011.Google Scholar
Kreiter, C. D., Stansfield, B., James, P. A., & Solow, C. (2003). A model for diversity in admissions: A review of issues and methods and an experimental approach. Teaching and Learning in Medicine: An International Journal, 15, 116–122. https://doi.org/10.1207/S15328015TLM1502_08.Google Scholar
Lloyd, M. (2004, February 13). In Brazil, a new debate over color. Chronicle of Higher Education. Retrieved from www.chronicle.com/article/In-Brazil-a-New-Debate-Over/4343.Google Scholar
lp_solve reference guide. (n.d.). Retrieved from http://web.mit.edu/lpsolve/doc/.Google Scholar
McMurtrie, B. (2004, February 13). The quota quandary. Chronicle of Higher Education. Retrieved from www.chronicle.com/article/The-Quota-Quandary/35480.Google Scholar
Overland, M. A. (2004, February 13). In India, almost everyone wants to be special. Chronicle of Higher Education. Retrieved from www.chronicle.com/article/In-India-Almost-Everyone/3612.Google Scholar
Pashley, P. J., & Thornton, A. E. (1999). Crafting an Incoming law school class: Preliminary results (LSAC Research Report 99-01). Newtown, PA: Law School Admission Council.Google Scholar
Pashley, P. J., Thornton, A. E., & Duffy, J. R. (2005). Access and diversity in law school admissions. In Camara, W. J. & Kimmel, E. W. (Eds.). Choosing students: Higher education admissions tools for the 21st century (pp. 231–249). Mahwah, NJ: Erlbaum.Google Scholar
Sarafraz, Z., Sarafraz, H., Sayeh, M., & Nicklow, J. (2015). Student yield maximization using genetic algorithm on a predictive enrollment neural network model. Procedia Computer Science, 61, 341–348. https://doi.org/10.1016/j.procs.2015.09.154.Google Scholar
Schmitt, C. M. (2009). Documentation for the restricted-use NCES-Barron’s admissions competiveness index data files (NCES 2010-330). Washington, DC: NCES.Google Scholar
Somers, P., Morosini, M., Pan, M., & Cofer, J. E. (2013). Brazil’s radical approach to expanding access for underrepresented college students. In Meyer, H.-D., St. John, E. P., Chankseliani, M., & Uribe, L. (Eds.). Fairness in access to higher education in a global perspective, pp. 203–221. Rotterdam: Sense. https://doi.org/10.1007/978-94-6209-230-3_12.Google Scholar
Walczak, S., & Sincich, T. (1999). A comparative analysis of regression and neural networks for university admissions. Information Sciences, 119, 1–20. https://doi.org/10.1016/S0020-0255(99)00057-2.Google Scholar
Zhang, R. (2010). Media, litigation, and regional discrimination in college admission in China. Chinese Education and Society, 43(4), 60–74. https://doi.org/10.2753/CED1061-1932430406Google Scholar
Zwick, R. (2017). Who gets in? Strategies for fair and effective college admissions. Cambridge, MA: Harvard University Press. https://doi.org/10.4159/9780674977648Google Scholar
Zwick, R., Blatter, A., Ye, L., & Isham, S. (2018). Using constrained optimization with an index of admission obstacles to increase the diversity of college classes. Manuscript submitted for publication.Google Scholar
Zwick, R., Ye, L., & Isham, S. (2016). Crafting a college class using constrained optimization. Paper presented at the annual meeting of the Association for the Study of Higher Education, Columbus, Ohio.Google Scholar
Zwick, R., Ye, L., & Isham, S. (2019). Using constrained optimization to increase the representation of students from low-income neighborhoods. Applied Measurement in Education, 281–297, https://doi.org/10.1080/08957347.2019.1660346.Google Scholar
- 1
- Cited by