## 2 Literature Review
### Risk Prediction in Student Performance Employing Machine Learning
While training took less than 1 s, the NB achieved a remarkable accuracy result of 76.65% while having significant mistake rates. While attempting to predict how students in an introductory programming course will perform, [PERSON] [24] took into account their activity record. This analysis suggested using an indirect foundation, such as automatically assessed criteria, to track student achievement over time. They created an evaluation method for student programs called WATWIN, which gives points for different kinds of work. A student's score was based on their responsiveness to and speed in fixing programming problems. For this analysis, we utilized data from 45 students' coding activity logs throughout 14 sessions. Each student's behavior was given a WATWIN score, which was subsequently employed in linear regression analysis. In the linear regression approach, the WATWIN score produced an accuracy of 76%. Prediction accuracy depends on a well-balanced dataset. In a balanced dataset, there is an equal attribute for each of the classes used in the prediction. [PERSON] [25] looked at the factors that change over time and their ability to predict online students' success. They suggested setting up an electronic warning system to identify at-risk pupils in asynchronous learning environments. They suggested using time-dependent factors as a crucial criterion in evaluating LMS students' progress. This paper's primary aims were to (i) explore data mining techniques for advanced warning, (ii) determine the implications of time-dependent factors, and (iii) choose data mining techniques with greater predictive ability. Three ML classification models, CART, LR, and AdaBoost, were tested using data from 330 learners enrolled in online courses in the LMS. Each instance in the dataset included 10 attributes, and the classifiers' effectiveness was measured by their accuracy as well as their resistance to making type I and type II mistakes. With an accuracy rate of 95% or above, the CART algorithm easily beats out the competition. [PERSON] [26] developed an ML framework to predict which students would be most likely to drop out of school or who would take too long to get their degrees. Information on students from two different schools and two different school districts is gathered using this structure. SVM, RF, LR, Adaboost, and DT are the five ML models put to use in experiments. Precision, recall, accuracy, and area under the curve (AUC) for binary classification are used to rate these methods. Every student is given a ranking that takes into account his/her assessed level of risk according to the model of categorization presented above. Based on the findings, Random Forest is the most effective method. The algorithms are ranked according to their accuracy and recall. Based on the suggested framework, the authors may recommend five important procedures for teachers to carry out in order to avoid the most common blunder. Some of these are (a) employing the FP-Growth algorithm to find patterns in the data, (b) employing the score's risk to rank the students, (c) adding a new field to the data and giving it a value of one (1) if the framework failed to predict it correctly and zero (0) otherwise, (d) computing the probability of error for each of the frequent patterns, and (e) sorting the patterns by error probability. [PERSON] [27] sifted through the student database at the school for information on its students. There were 1547 instances in the collection, and each had 10 characteristics. Various departments, high school diplomas, grades at midterm and on lab tests, seminar and assignment grades, seminar and assignment engagement and performance, seminar and assignment scores, homework and final grade averages, and final grade averages were all collected using the qualities chosen. Data categorization utilized DT and ID3 Decision Tree, two ML models. Then, the Weka data-mining tool was used to conduct experiments. With the use of the collected data, they determined that the midterm property should serve as the tree's starting point.