[Retracted] Enhancement of Predicting Students Performance Model Using Ensemble Approaches and Educational Data Mining Techniques

Table 1

Models, approaches, and methods that are used to predict a student’s performance.

No.	Technique/method/model	References	Advantages	Disadvantages
1	Support vector machine	[13–16]	In base feature space, it is well suited for nonlinearly separable data.	Classification necessitates a large amount of memory, as well as a high level of complexity.
2	Decision tree	[15, 17]	It is simple to put in place, understand, and use.	Because a slight change in the data can result in a different decision tree, the time required for searching is significant.
3	Regression	[15, 16, 18, 19]	Perform better when dealing with continuous attributes and linearly separable data.	Outliners have an effect on the data; hence, it is not suitable for nonlinearly separable data (overfitting).
4	-nearest neighbor	[14, 19–22]	Work well with nonlinearly separable classes and perform well in multimodal classes.	The extra time required for determining the nearest neighbor in a large training dataset.
5	Naïve Bayes	[18–20, 23]	Improve categorical input variable outcomes and multiclass prediction performance.	When dealing with little amounts of data, the algorithm’s precision suffers.
6	Neural network	[15, 18, 19, 24]	No retraining is required because it learns events; it is applicable to real-world issues, and there are few parameters to alter, making it simple to use.	Large networks necessitate a long processing time, and determining how many neurons and layers are necessary is difficult.