Apply Machine Learning Algorithms to Predict At-Risk Students to Admission Period

Abstract

Educational organizations have a high burden of treating students with poor academic success (At-risk students). Many methods are pursued to help this group of pupils, such as psychological, a proper schedule for the vulnerable pupil, recall, personal training, mock-tests, private education by direct studies, or the success centers. However, these approaches are not enough to deal with the dilemma, since other variables are impacting the success of the learner, which may be their family problems, their cognitive style, Their previous academic accomplishment, the cornerstone of the college level. This paper explores the effect of pre-college academic accomplishments on the success of college students in the computer program. The pilot study predicts student academic success based on their math (EmSAT Math), English (EmSAT English), and High School Average (High School Average) knowledge and skills acquired by students before college entry. The study covers 120 students. After cleaning up the available data, we are left with 112 instances used to train machine learning models to predict learner performance in the computer program. The qualified models achieved a high precision of 90.5 percent (Naive Bayes, Decision Tree, Random Forest), while the Logistic Regression achieved a forecast accuracy of 78.1 percent. The main symptom for the student's academic status was the score in English, followed by the score in Math. The research has shown that the high school score is not the secret to predicting students with academic status at their college stage.