Before schools can provide kids with the extra help they may need, they first need to identify which kids need extra help. This article proposes ML using linear and random forest regression based on multiple variables to determine what students are at risk of not graduating high school in four years. This method of predicting was 74% and 75% effective respectively, as opposed to the previous rule based model which was only 38% effective. Not only is it more effective, but it saved time and resources.
https://dl.acm.org/doi/10.1145/2723576.2723619
Before schools can provide kids with the extra help they may need, they first need to identify which kids need extra help. This article proposes ML using linear and random forest regression based on multiple variables to determine what students are at risk of not graduating high school in four years. This method of predicting was 74% and 75% effective respectively, as opposed to the previous rule based model which was only 38% effective. Not only is it more effective, but it saved time and resources. https://dl.acm.org/doi/10.1145/2723576.2723619