An Analytical Exploration of the Student Debt Crisis

An Analytical Exploration of the Student Debt Crisis

The article describes AI model simulations applied to better interpret the socioeconomic reasoning of student loans that leads to their financial dependence. This project is the culmination of work performed with Omdena and ShapingEDU.

The article describes AI model simulations applied to better interpret the socioeconomic reasoning of student loans that leads to their financial dependence. This project is the culmination of work performed with Omdena  and ShapingEDU .

On-boarding and familiarizing with the project

This project was a continuation of my collaborations with the Omdena team where I looked into creating some insightful conclusions from existing data to shed light on the pressing issue of student debts. Team members consisted of individuals from all walks of life, from many diverse backgrounds and experiences. Members held an introductory meeting to get on board with the deliverables and discuss the division of work to understand what the focus of the groups should be.

I petitioned for the use of a group that would analyze socioeconomic factors for student debts as well as financial literacy to grasp the reasons, dynamics, and elements that drive such practices and potential methods to remedy them. With enough members and interested individuals who showed a similar yearning for these objectives, a group was formed and deliverables were distributed with the purpose to build useful results. Some of the more important questions we aimed to answer from these simulations included:-

1. Are there any specific disciplines and backgrounds that have a greater correlation for racking up debt over prolonged periods?

2. What is the bigger influence of distance and proximity on obtaining jobs and how do economic profiles assist in finding jobs in an environment where debt continues to rack up?

3. Can debt to earning ratios be modeled and connected to their inputs within a reasonable error ratio to be used by others to determine what professions or jobs they should take up to pay back in time?

4. Does adding more educational credentials play a role in fastening the debt repayment process and if so, is this common across all disciplines?

5. What are the effects of dropouts and candidates with incomplete credentials against those with complete educational credentials?

Several projects have been conducted that look into the student debt crisis but tend to falter due to biases or singular thinking that restricts the public’s ability to understand what the data means. This project is unique because it combines the experiences of a large group of analysts and researchers.

Challenges Addressed and Worked

While visualization and graphical representations of the datasets were achieved early on, a great deal of time had to devoted to cleaning and factoring the datasets to appropriate columns to make sense of what it represented. We faced another challenge of having an unbalanced dataset that was aimed towards U.S. students far more than international students, which required us to explore other avenues and sources. The solution to segmentation and filtering the appropriate columns and rows needed further steps to remove missing values and convert categorical variables to encoded keys that the system could understand.


Education In The Age Of Debt

The team and the project members had discussed on an ironclad list of objectives that had to be met by the end of the entire project while smaller sub-objectives continued to change with time to either fit the data or to keep up with the demands of workable models that were becoming computationally slow.

Early on, the datasets that were to be used for the project were selected based on the number of factors and parameters that could be coded and modeled using different. We were specific not to restrict ourselves to datasets with numeric outputs and use classification based output data. Two major datasets that were used for this part of the project looked at the possibility of loan defaults, debt to earnings ratios, interest rates, and the profiles of financial literacy among applicants. The data and its results were finally connected with visualization tools and geographic data to come up with useful results for the best locations for repaying debts.

Another major part of the project that looked at was socioeconomic factors. It was aimed at finding information about the alumni records from various institutes and building models for their financial and occupational outcomes after graduation. This dataset looked at parameters such as location, discipline studied, race, economic background, loan amounts, institution type, degree type, time taken to obtain the first job after graduation, and so on. But despite the vast amounts of data we had at our hands, we found additional hurdles as we went on.

machine-learning student-loans education-reform artificial-intelligence students

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