## 2 Literature Review
### Risk Prediction in Student Performance Employing Machine Learning
Measures of precision, recall, and F-score were used to assess the efficacy of the predictors. From 2004 to 2011, data for 3000 students were gathered; following pre-processing and redundancy reduction, this number was reduced to 776 learners. Only 690 of the 776 students were able to completely finish their degree programs. Expenditure, income, student demographics, and asset data were separated out of the total of 33 factors. Spending on natural gas, electricity, self-employment, and location emerged as the most significant factors predicting student success in the research. With an F1 score of 0.867, the SVM classifier easily beats out the competition. In order to determine what factors into the PISA 2005 result, [PERSON] [34] used ML methodologies. In this work, the author examined PISA 2005 data from a wide range of countries, including the United States, the United Kingdom, Australia, Spain, Canada, Italy, Japan, and France. The purpose of this research was to identify the factors at play in both the students' and the schools' environments that have a bearing on students' performance, with the suggested methods operating in two stages. First, they used a multilevel regression tree that nested individuals inside their respective schools to identify factors at the student level that are predictive of academic performance. After that, they estimated schools' added value to better understand what factors are associated with student success via the use of regression trees and boosting methods. The PISA 2015 dataset was utilized, which included data from nine countries and included a total of 19 characteristics (eighteen at the school level and one at the student level). [PERSON] [35] used eight distinct ML algorithms to compare academic aspects and to evaluate the importance of nonacademic variables, such as demographic information. They used data collected from an Indian technical college, which included demographics, course information, and student activities for a total of 6807. To remedy the asymmetry of the data, the team used synthetic minority oversampling techniques. They asserted a 93.2% F1 score using the DT, 90.3% using the Lr model, 91.5% using the MLP model, 92.4% using the SVM model, 92.4% using the AdaBoost model, 91.8% using the Bagging model, 93.8% using the RF model, and 92.3% using the Voting model. The authors also argued that students' success in school is affected not only by their academic characteristics, but also by their socioeconomic background, and they proposed combining non-academic factors with academic ones to predict students' outcomes. Using AutoML, [PERSON] [36] improved the accuracy of student performance prediction by capitalizing on characteristics collected in the lead-up to the commencement of the new curriculum. With AutoML, they were able to reduce the false prediction rate while maintaining an accuracy of 75.9% and a Kappa value of 0.5. They concluded that AutoML should be used by researchers in this area to find the best possible learner performance prediction model, particularly when starting with pre-start data. To support students in imminent need of assistance, they recommended using pre-admission data to initiate intervention and consultation sessions prior to beginning academic progress. Because of the uneven distribution of the available data, they used the SMOTE pre-processing approach and the automatically created ensemble methods to make accurate predictions about which students will fail. The authors recognized SMOTE's overgeneralization weakness and suggested ways to mitigate the unbalanced data issue without resorting to a less than ideal solution: a more balanced dataset. Using past data, [PERSON] [37] advocated using ML techniques to predict learners' ultimate grades. They used information gathered from the universities of Ecuador's computer engineering departments. The collection of a large amount of high-quality data was the primary objective of this study.