Mining of Completion Rate of Higher Education Based on Fuzzy Feature Selection Model and Machine Learning Techniques

Abstract

In the context of the great change in the labor market and the higher education sector, great attention is given to individuals with an academic degree or the so-called graduates class. However, each educational institution has a different approach towards students who wish to complete their university degree. This study aims at (1) identifying the most important factors that directly affect the completion, and (2) predicting the completion rates of students for university degrees according to the system of higher education in the United States. Unlike previous studies, this project contributes to the use of the fuzzy logic technique on three methods for feature selection, namely the Correlation Attribute Evaluation, Relief Attribute Evaluation, and Gain Ratio Method. Since these three methods give different weight to the same attribute, the fuzzy logic technique has been used to get one weight for the attribute. A great challenge faced throughout this study is the curse of dimensionality, because the college scorecard dataset launched by the US Department of Education contains approximately (8000) educational institutions and (1825) features. Applying the method used in this study to identify important features lead to their reduction to only (79). Accordingly, two models have been used to predict the completion rates of students for their university studies which are the Random Forest and the Support Vector Regression with a Mean Absolute Error (MAE) value of (0.068) and (0.097) respectively.

Forecasting Gender in Open Education Competencies: A Machine Learning Approach

“…Understanding existing gender differences in skill development is becoming increasingly important to gain valuable information on how to close the gender gap. The analysis of specific sociodemographic characteristics in machine learning studies in education is still in its infancy [48], [49]. Gender prediction using machine learning models mainly focused on gender prediction of educational leadership [50]; female models and reinforcement in STEM [51]; exploring gender differences in learning computational thinking [52]; the intersection of the academic gender gap [53]; and gender stereotyping in academic dropout [54].…”

Identifying Critical Factors When Predicting Remedial Mathematics Completion Rates

“…This is likely because one cannot complete a course, a sequence, or a program without first being retained. In a variety of disciplines in education, ML techniques have been used to understand student retention and completion (e.g., Chai & Gibson, 2015;Delen, 2010;Đambic ́et al, 2016;Jia & Mareboyana, 2014;Lykourentzou et al, 2009;Nouri et al, 2019;Wotaifi, 2019). Most research on completion and non-completion is usually conducted through surveys.…”