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The purpose of our research project is to explore and analyze an anonymized data set of 2,300 School of Information Technology students who attended ISU between 1996 and 2016 and present a visualization of student retention predictors. Visualizing various factors influencing student retention required knowledge of both computer programming and data analysis. We used Microsoft Power BI for data visualization and applied Python programming language to explore potential predictors leading to Information Technology (IT) student retention. Moreover, Power BI was used to create a dashboard which helped us visualize demographic attributes. We later conducted predictive analytics (i.e., multiple linear regression and logistic regression) using Python. We found that logistic regression is most suitable for our student retention data. Exploring these factors through various data science techniques helped us better understand the relationships between student retention and other factors. Insights for our data analyses and retention strategies are provided.
Nowlin, Preston; Benefiel, RJ; and Hazzard, Evan, "Exploring Information Technology Student Retention" (2021). Information Technology. 4.