# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods
### Federated Learning-Based Authentication
Attention Layer: SE blocks, known for their simplicity of implementation, enhance feature extraction within the model and facilitate classification. The squeeze operation in this layer aims to capture the overall features of a channel, with each feature map from the convolutional layer. Primary Caps Layer: After the SE block, PrimaryCaps receives each feature map and attention weight. The PrimaryCaps layer differs from the standard convolution layer. After this layer, we can produce capsules, also known as vectors, which can store significant information. The SE block then produces attention feature maps by multiplying the original feature maps by the learned channel weights to perform a scale operation. DigitCaps Layer: The DigitCaps layer stores Ponzi and non-Ponzi capsules. Vectors represent the ultimate result. Capsule networks squash. The likelihood of an entity's presence is determined by the length of the output vector, while retaining its orientation. To generate recommendations for a student, the relevant student information is extracted from the multi-disciplinary ontology graph, which illustrates the relationships among various factors influencing student performance and their actual performance out comes. Using these queried data, the proposed Att-CapsNet recommender system offers a tailored set of recommendations for each student. For example, a student at risk of dropping out is analysed in conjunction with their performance-affecting factors and may be recommended various options, such as scholarship opportunities, accommodation arrangements, and more, all personalised to their needs. In the case of students facing the risk of failing or achieving lower marks, the system assesses the behaviour of course instructors (CIs) and modifies their teaching styles. This modification may involve simplifying content through animations, incorporating gamified e-learning elements, offering multiple attempts and feedback-based question-and-answer sections (i.e., retests), and so on. Additionally, the proposed approach continually monitors and tracks student performance on a weekly basis, aiming to enhance performance and provide recommendations to boost student engagement in the course. In a different context, the lightweight encryption algorithm (LWEA) introduces a secure, computationally efficient encryption method. This encryption method consumes less power due to its reduced key size. The process involves three distinct algorithms:

* Secret key generation and text encryption
* Secret key encryption and text encryption merging
* Text decryption using a secret key

The strategy relies on the one-time pad technique, where a random number generator generates the secret key. Notably, the secret key is transmitted within the encrypted file itself, adding an extra layer of security to the process. All information, including student authentication details, engagement status, performance status, ontology graph data, and recommendation outcomes, is securely stored within the blockchain in an encrypted format utilising the Lightweight Encryption Algorithm (LWEA) [47]. This approach ensures the utmost security and confidentiality, making the data unreadable to anyone without the proper decryption key. The use of LWEA, known for its efficiency in environments with limited computational resources, alongside blockchain technology, provides a robust and tamper-resistant solution, ensuring sensitive information remains protected from unauthorised access, breaches, or tampering, thereby enhancing the overall security and integrity of the e-learning platform. ## 5 Experimental Results

To simulate the proposed research method, JDK 1.8, Netbeans 12.3, and Wamp Server 2.0 (MySQL 5.1.36) were utilised. These tools are efficient and provide all the necessary specifications for the proposed technique. In evaluating the system, the study used the Open University Learning Analytics dataset (OULAD), developed by [PERSON] et al. in 2017 [48], specifically designed for e-learning testing environments.