# Visual Data and Pattern Analysis for Smart Education:
## 3 Problem Definition  
Our previous work, AISAR [38], developed a model using artificial intelligence algorithms to enhance academic performance in e-learning environments. This model encompassed several processes: student authentication, score estimation, clustering, performance prediction, and personalised recommendations. Authentication was conducted through PINs, passwords, and IDs, while student scores were estimated via a recurrent neural network. Students were then categorised into low, average, and excellent groups using a density-based spatial clustering algorithm. Performance prediction employed a threshold-assisted map reduction method to categorise students as very low, low, and normal. For generating recommendations, the State-Action-Reward-State-Action (SARSA) algorithm was implemented. However, this model had several limitations: the authentication approach was susceptible to modern security threats; the clustering method inadequately classified student involvement, leading to high false-positive rates; and the performance prediction lacked real-time data processing capabilities, resulting in reliability and availability issues. Additionally, the SARSA-based recommendation system did not adequately cater to the needs of below-average students requiring personalised support.  
The authors in [39] propose a machine learning-based model for predicting student performance, utilising behaviour and learning information. This process includes data pre-processing, feature extraction, classification, and the development of a machine-based recommendation model. This model is built upon features such as learning behaviour consolidation, interaction, knowledge acquisition, and preparation. However, the study fails to consider crucial factors, such as student interactiveness, participation, emotions, and skills, which could compromise the effectiveness of predictions. Additionally, the study underscores the necessity of addressing issues related to complexity, overfitting, and underfitting to enhance the accuracy of prediction outcomes.  
In [40], the authors proposed using personalised feedback and machine learning algorithms to predict learning behaviours in e-learning modules. Data were collected from performance indicators, educational metrics, and demographic variables. Pre-trained gated recurrent units were employed to classify students into pass or fail categories. Although personalised feedback was provided, these methods did not significantly improve the learning outcomes for struggling students. The study predominantly focused on identifying aspects and behaviours related to students, rather than considering teacher-related aspects that could influence student performance.  
The authors in [41] proposed a deep learning algorithm to predict student performance and generate recommendations based on an early warning system. The data underwent pre-processing, grade prediction, and clustering using the k-nearest neighbour algorithm. The findings indicated that deep neural networks surpassed the performance of other machine learning models. However, the study faced limitations, including the omission of student and teacher legitimacy, the lack of personalised recommendations based on grades, and the lower accuracy and high computational complexity associated with the k-nearest neighbour algorithm.  
The study [42] developed an automated framework to generate recommendations based on student academic performance using machine learning algorithms. This framework employed natural language processing for semantic analysis. Data were collected from user profiles, pre-processed, and analysed using an agent-based methodology. The results demonstrated that agent-based predictive recommendations surpassed the performance of other methods. However, the study did not consider factors such as student engagement results, teacher behaviour, and effective tracing of recommendations for further assistance.