# Visual Data and Pattern Analysis for Smart Education:
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Objective** & **Model or Algorithm** & **Limitations** \\ \hline
[22] & Analysing student engagement in online classes & CNN & Susceptibility to overfitting and underfitting issues with the adoption of CNN for distraction and facial emotion analysis \\ \hline
[23] & Assessing human emotions to enhance distraction-free e-learning & Bayesian Classifier & Focusing solely on student emotions, without incorporating interactiveness and skills, limits performance prediction \\ \hline
[24] & Predicting student dropout risk in e-learning using a Hidden Markov Model & HMM & Neglect of key demographic factors impacts prediction accuracy \\ \hline
[25] & Designing an early warning system for student performance using machine learning & Gradient Boosting & Limited analysis of demographic and non-academic factors \\ \hline
[26] & Predicting student performance using ANN & ANN & Prone to overfitting and computational complexity \\ \hline
[27] & Resolving course selection anxiety in e-learning through ontology-based course recommendations & Ontology-based recommendation system & Prone to overfitting and computational complexity \\ \hline
[28] & Developing a robust recommendation system for adaptive e-learning using various autoencoders & Collaborative Filtering-based Deep Autoencoders & Handling massive datasets with machine learning algorithms introduces complexity \\ \hline
[29] & Performance prediction using machine learning and fuzzy methodology & Fuzzy SVM & Handling massive datasets with machine learning algorithms introduces complexity \\ \hline
[30] & Designing an adaptive e-learning environment using DRL & DQN & Vulnerability to login manipulation by malicious attackers \\ \hline
[31] & Designing an e-learning recommendation system using Deep Learning and Knowledge Learning and Knowledge Graphs & Deep learning and Knowledge relationship evaluation, leading to suboptimal recommendations \\ \hline
[32] & Providing robust online course recommendations using DNN & DNN & Unsuitability of K-Means clustering for data with varying density and size \\ \hline
[33] & Identifying learning styles using machine learning algorithms & Naive Bayes, Linear Discriminant Analysis, K-Nearest Neighbors, Random Forest, Logistic Regression, Decision Tree, Support Vector Machine & Limited factors, increased false positive rates \\ \hline
[34] & Predicting student performance using logged data in a learning management system & Support Vector Machine, Multi-layer Perceptron, Logistic Regression, Gaussian Model, Decision Tree Model & Exclusively relying on previous grades overlooks other factors influencing student academics \\ \hline
[35] & Developing a student performance predictor using machine learning & Decision Tree Regression, Decision Tree Classifier & Limited assessment of demographic and non-academic factors in performance prediction \\ \hline
[36] & Academic performance prediction using machine learning agents & Logistic Regression, Decision Tree, Random Forest, Naive Bayes & Limited factors, increased false positive rates \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the literature survey.