Analyzing feature importance for a predictive undergraduate student dropout model

Worldwide, one of the main concerns of universities is to reduce the dropout
 rate. Several initiatives have been taken to avoid this problem; however, it
 is essential to recognize at-risk students as early as possible. This
 article is an extension of a previous study that proposed a predictive model
 to identify students at risk of dropout from the beginning of their
 university degree. The new contribution is the analysis of the feature
 importance for dropout segmented by faculty, degree program, and semester in
 the different predictive models. In addition, we propose a dropout model
 based on faculty characteristics to try to infer the dropout based on
 faculty features. We used data of 30,576 students enrolled in a Higher
 Education Institution ranging from years 2000 to 2020. The findings indicate
 that the variables related to Grade Point Average(GPA), socioeconomic
 factor, and a pass rate of courses taken have a more significant impact on
 the model, regardless of the semester, faculty, or program. Additionally, we
 found a significant difference in the predictive power between Science,
 Technology, Engineering, and Mathematics (STEM) and humanistic programs.