## 2 Literature Review
### Risk Prediction in Student Performance Employing Machine Learning
In contrast to other research, [PERSON] [28] studied prediction models to detect under-performing students in a standard-based education setting. Additionally, they used the data from the first-year engineering program at a Midwestern US University in 2013 and 2014 to implement feature selection techniques that reduced the feature space. The student progress data collection included things like quiz scores, homework grades, teamwork marks, project checkpoints, quantitative modeling, and exam results. Classifiers from KNN, LR, NB, SVM, MLP and DT were among the six classifiers that were chosen in this study. Overall accuracy, pass-rate accuracy, and failure-rate accuracy were used to assess the performance of these classifiers. Features having a Pearson's correlation coefficient value greater than 0.3 were employed in the prediction procedure as part of the feature selection approach. In contrast, NB models trained with 16 characteristics performed better (88 percent accuracy). Additionally, [PERSON] [29] predicted learners' GPA utilizing CF, MF, and RBM in a comparable assessment. This analysis relied on data gathered from Information Technology University (ITU) in Lahore, Pakistan. A feedback model was presented to determine the extent to which a student had grasped the material covered in a given class. In addition, they proposed a method for tuning the Hidden Markov model (HMM) such that it may be used to foretell how well students would perform in a given class. The experimental data were divided into a 70% training set and a 30% testing set. RMSE, MSE, as well as Mean Absolute Error were used to rank the ML-based classifiers. For this dataset, RBM performed well, with mean squared errors (MSEs), Mean Absolute Errors (MAEs), and Root-Mean-Squared Errors (RMSEs) all below 0. [PERSON] [30] looked at how well various classifiers measured up while analyzing student work. In this analysis, they looked at five different ML-based classifiers and compared their features. Some examples of classification models include NB, J48, Bayesian Networks, NN, and ID3. All of these methods were implemented in Weka and utilized in the tests. Data for this study were culled from a college database containing 225 cases, each of which had 10 characteristics. This analysis demonstrated that the Bayesian Network is the best prediction method for real-world use. [PERSON] [31] proposed a novel ML technique with two standout characteristics. The first was a multi-tiered framework for forecasting that takes into account pupils' dynamic performance behaviors. Multiple bases as well as ensemble classifiers composed the layered structure. The suggested method's second key aspect was its data-driven strategy for determining lecture topics. Records for 1169 UCLA undergraduates were included in the collection, which focused on the departments of Aerospace and Mechanical Engineering. In terms of MSE, the suggested technique performed well. Using data collected at the University of Minho and consisting of 33 characteristics, [PERSON] [32] conducted a similar review in which he compared the efficacy of SVM and KNN supervised learning classifiers. Before performing statistical analysis, the dataset was transformed from categorical to numeric forms. Questionnaire and teacher reports from two Portuguese schools formed the basis of the dataset. There were 395 unique occurrences in the original dataset, and its 33 characteristics included nominal, binary, and numeric attribute distributions of 4, 13, and 16, respectively. In the experiment, several data division sets were employed to test the methods, and the Weka program was used to do so. When utilizing a 10-fold cross-validation and partition ratio, the SVM was shown to have a good degree of accuracy. [PERSON] [33] investigated how advanced learning analytics may be used to forecast student success. Scholars in Pakistan were taken into consideration. In this study, they compared the Bayes Network and NB against the discriminative CART, SVM, and C4.5 models.