The Problem and the Opportunity

The current college admissions process is flawed. Despite the best efforts of admissions officers, there is little proof to suggest that current application review processes are robust and result in ideal class compositions. A 1996 Northwestern University study found that the likeliest determinants of admission were the standard test scores, grades, and information found in one’s application – not the unique characteristics admissions officers claim to look for in essays and interviews as more of a complete evaluation of the candidate. Perhaps even more worrisome is that admissions offices, outside of those at some of the top universities, do not evaluate the outcomes of application decisions – such as the success of an admitted student or the future financial benefits to the institution of that student’s acceptance – and incorporate that feedback into their admission evaluation criteria.

In addition, the application review process at most universities is still largely manual, requiring admissions officers to read and evaluate tens of thousands of applications multiple times each year. Significant time is spent on even the most clear-cut of cases . In a University of Texas study of how a machine learning solution could aid PhD application review processes, it was observed that reviewers spent over 700 hours reviewing a pool of 200 applicants. Now, consider that many of the top universities receive tens of thousands of applicants.

We see a clear opportunity for a machine learning-based solution to address the existing flaws in the college admissions process. Not only is there a large capturable market with over 3,000 institutions of higher education in the U.S. that all face this admissions evaluation issue, the number of college applications continues to increase, which will only exacerbate the issues described above.

 Our platform, HiPo!, will help provide significant time and human resource savings to university admissions offices. In addition, it will be trained using historical application and student performance data to help admission officers optimize their admissions evaluation process across a number of outcome-based factors (e.g., future earnings potential, yield likelihood, philanthropic giving potential).   

The Solution

The proprietary algorithm will utilize a semi-supervised machine learning model. The supervised elements that the model will optimize for are the quantifiable outcomes such as future earnings potential, yield likelihood, and philanthropic giving potential. However, given the vast expanse of data that the model will be trained on through years of qualitative data from student essays and interview transcripts, there are other elements that in an unsupervised way, the algorithm can make associations and clusters from to derive additional predictive value. These elements are not as measurable such as creativity or diversity of thought – both things that an admissions committee would value in a class. However, over time if the algorithm can add additional measurable information to admissions officers on these dimensions, they would provide additional evaluative data.

The inputs into the core product are traditional quantitative metrics, such as GPA, standardized testing scores, etc., in addition to qualitative inputs such as essays, interview transcripts, recommendations, and resumes. By creating a robust feedback loop by measuring the success of various students over time based on the HiPo! evaluation criteria, the algorithm will be able to estimate outcomes and provide admissions officers with quantifiable score reports:

This is merely a prediction of the likelihood of future outcomes based on historical results of similar profiled candidates, not a pure measure of an individual’s current attributes.

Empirical Demonstration

To validate the effectiveness of the machine learning algorithm and to validate the hypothesis that certain characteristics and patterns present in an candidate’s application, essays, and interviews are reflective of future outcomes, the HiPo! team would perform the following demonstration pilot. Partnering with an institution of higher education, such as the University of Chicago Booth School of Business, HiPo! would collect historical applicant records from 1950-1992. Half of this data would be randomly selected to train the algorithm, under the supervised and unsupervised learning methods described above. The algorithm would then be applied to the remaining half of the data. The predictive output of the solution would be measured against actual outcomes of the students evaluated in the sample. For instance, if Michael Polsky MBA ‘87 was evaluated as part of the sample, a successful algorithm would predict that he would have both high career earnings potential and strong likelihood of philanthropic behavior. It is critical that the data used for the demonstrated be at least 20 years old, so that outcomes such as future career success and philanthropic giving can be accurately assessed.

 
Sources

Cole, Jonathan R. “Why Elite-College Admissions Need an Overhaul.” The Atlantic. Atlantic Media Company, 14 Feb. 2016. Web. 13 Apr. 2017.

Fast Facts. N.p., n.d. Web. 13 Apr. 2017.

Miikkulainen, Risto, Waters, Austin. “GRADE: Machine Learning Support for Graduate Admissions.” Proceedings of the 25th Conference on Innovative Applications of Artificial Intelligence, 2013.