The Impact of Predicting Academic Success in Higher Education: A Review

This review presents the issues pertaining to higher education as well as in-depth schooling, in particular the connections between the two, it is essential to take into consideration the possibility of predicting the academic success of students since this is a crucial component that must be taken into consideration at all times. With the help of the capability to forecast their level of success, students are given the opportunity to select the courses with subsequent study plans that will be of the greatest benefit to them personally. Because of the availability of this talent, this can now be accomplished. It provides educators and school administrators the chance to keep an eye on students, which, in turn, enables them to offer increased assistance to pupils, combine educational initiatives to produce the best possible outcomes, and forecast the extent to which kids will be able to successfully complete their schooling. Student forecasting has numerous advantages, one of which is that it leads to a reduction in the number of official warning signals for school expulsions that are brought on by inefficiency. This is only one of the many advantages of student forecasting. This is but one of the several advantages that may be gained from student forecasting. Students are able to see what their future holds if they take the time to choose their classes carefully and devise study strategies that make the most of their individual skill sets, areas of interest, and areas of expertise. The Support Vector Classifier proved to be the most useful instrument in the course of this investigation due to the fact that it had values of 0.888 for accuracy, precision, recall, and f1 respectively for each of those categories. This was because it had the capability of accurately classifying the data. These results are evidence that the data were categorized with a high degree of precision, and they are presented here for your consideration. Throughout the entirety of this investigation, a number of distinct approaches to machine learning, including ensemble, logistic regression, random forest, AdaBoost, and XG Boost, were utilized. These approaches were used to classify the data.