Abstract
BackgroundThis paper explores machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors and a ‘high’ earners’ class.MethodsIt examines the alum sample data obtained from a survey from a multicampus Mexican private university. Survey results include 17,898 and 12,275 observations before and after cleaning and pre-processing, respectively. The dataset comprises income values and a large set of independent demographical attributes of former students. We conduct an in-depth analysis to determine whether the accuracy of traditional algorithms can be improved with a data science approach. Furthermore, we present insights on patterns obtained using explainable artificial intelligence techniques.ResultsResults show that the machine learning models outperformed the parametric models of linear and logistic regression, in predicting alum’s current income with statistically significant results (p < 0.05) in three different tasks. Moreover, the later methods were found to be the most accurate in predicting the alum’s first income after graduation.ConclusionWe identified that age, gender, working hours per week, first income and variables related to the alum’s job position and firm contributed to explaining their current income. Findings indicated a gender wage gap, suggesting that further work is needed to enable equality.
Highlights
Higher education institutions seek to boost their alumni outcomes after graduation
One of the experiments conducted in this study included a prediction of realised earnings. This model was performed with Ordinary Least Squares (OLS) regression and achieved a 16% Coefficient of determination (R2)
The researchers used quantile regression methods. They indicated that the advantage of quantile regressions is that it allows examining a more comprehensive picture for different quantile wage groups
Summary
Higher education institutions seek to boost their alumni outcomes after graduation To validate whether this goal is being accomplished, there is value in collecting data from their alumni and identifying patterns between those who achieved their expected outcomes and those who did not. Understanding the factors that may favour some alumni will help give them equal opportunities to achieve their economic objectives Many institutions survey their graduates to collect information on their post-graduation outcomes, such as their income and socioeconomic status [1,2,3]. The actions taken to analyze the results rarely include data mining to obtain insights regarding features that can have a higher relationship with the outcome This is especially true for actionable features that can be boosted with activities performed during students’ lives on campus. This paper explores machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors and a ‘high’ earners’ class
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