# Visual Data and Pattern Analysis for Smart Education:
## 2 Literature Review  
This literature review delves into the realms of e-learning, particularly focusing on student performance assessments and recommendation generations. Recent studies have significantly contributed to understanding and enhancing student engagement and performance through various technological and methodological innovations. The exploration of emotional engagement in e-learning environments has been a pivotal focus. For instance, the study identified in [22] employed machine learning algorithms, specifically convolutional neural networks (CNNs), to analyse students' emotional states through eye movements and facial expressions during online learning sessions. Thisapproach delineates a method for categorising student engagement levels, albeit with challenges, such as overfitting. Similarly, the research presented in [23] undertakes the classification of human emotions into distinct categories like surprise, anger, and sadness using a Bayesian classifier, underscoring the importance of emotional assessment but also noting limitations when excluding interactive and skill-based parameters. In the domain of predictive analytics for identifying at-risk students, several methodologies have been employed. The hidden Markov model used in [24] exemplifies an intelligent approach to predicting student dropout risks, although it notably lacks incorporating demographic features, which could enhance prediction accuracy. Likewise, refs. [25; 26], have advanced the application of gradient boosting and artificial neural networks (ANN) for early warning systems and performance analysis, respectively, each discussing specific challenges, such as handling large datasets and computational demands. The development of recommendation systems to enhance e-learning experiences has seen innovative applications of machine learning and deep learning techniques. Studies like [27; 28] have contributed to this field by utilising ontology and autoencoders for course recommendations, highlighting the balance between leveraging historical data and addressing individual learner needs. Similarly, the combination of machine learning techniques with fuzzy methodologies in [29] presents an advanced approach for designing performance prediction systems. By integrating fuzzy logic, this model aims to refine the accuracy of predictions and recommendations, addressing the complexity of student data and learning paths. In the context of adaptive learning environments, ref. [30] introduces the application of Deep Q-Network (DQN) algorithms from Deep Reinforcement Learning (DRL), proposing a method for distributing learning rules and exercises intelligently. However, this approach is not without its challenges, particularly concerning the security and integrity of the learning system, as highlighted by potential vulnerabilities in the login methodology. Further, the integration of deep learning for personalised e-learning environments, as discussed in [31; 32], showcases the shift towards more adaptive and learner-centred approaches, although they also reveal gaps in evaluating and integrating learner information effectively. Learning style identification has also garnered attention, with [33] employing various machine learning algorithms to detect and cater to different learning preferences, signifying a move towards more personalised education. However, the challenge remains in accurately capturing and responding to the diverse learning modalities of students. Moreover, the application of machine learning extends into predictive analytics for academic success, with studies like [34; 35; 36] exploring different algorithms and data preprocessing techniques to forecast student performance. These studies underline the potential of predictive analytics, while also pointing out the limitations of relying solely on historical academic data or a narrow set of predictive factors. In the domain of Information Retrieval and Natural Language Processing (NLP), a new study [37] presents a fake news detection model called MST-FaDe.