# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods
### Federated Learning-Based Authentication
**Pseudocode 2: TSE Calculation**

**Input**: \(\mathfrak{R}\)(interactive data), \(\mathfrak{U}\) (threshold)

Initialise \(\mathfrak{H}\) to 0.0

For each daily count in \(\mathfrak{R}\):

If \(\wp\) (probability or some related metric) > \(\mathfrak{U}\):

Calculate H using Equation (9) for probability above \(\mathfrak{U}\)

If \(\mathfrak{H}<some\ \mathfrak{U}_{value}\):

Interactiveness = \"Less Interactive\" (_e_low)

Else:

Interactiveness = \"Highly Interactive\" (_e_high)

**Output**: classify \(\mathfrak{R}\) based on \(\mathfrak{H}\)

#### 4.2.2 Student Academic Emotion Acquisition

Enhancing data quality in e-learning platforms can be achieved through advanced technological measures, including real-time feedback systems, pre-processing algorithms, and systematic quality checks. Specifically, the YOLOv6 algorithm [45] significantly improves the handling of varying image qualities by integrating features from SEC-YOLO and MCE-YOLO [46], focusing on essential facial regions like the eyes, forehead, and cheeks for emotion analysis. This deep learning approach enables the precise classification of student emotions--frustration, confusion, boredom, and engagement--by leveraging robust feature extraction that remains effective even in lower-quality video streams. Intelligent Software Agents (ISAs) utilise the YOLOv6 algorithm to monitor and analyse students' facial emotions daily, employing the threshold-based Shannon Entropy (TSE) method for real-time emotion classification based on the clarity and visibility of key facial features. This dual approach, combining the YOLOv6's advanced detection capabilities with the TSE method, allows for accurate emotion categorisation and the compilation of weekly emotional insights without compromising data quality or student privacy. Overall, the integration of YOLOv6, which marries single-channel detection with multi-channel depth for enhanced object recognition, alongside a targeted facial feature analysis, ensures a comprehensive and nuanced understanding of student emotional states. This methodological synergy not only improves the accuracy of emotion detection, but also supports a more personalised and responsive e-learning environment. To maintain high-quality emotional data collection, providing alternatives for students who are unable or unwilling to use cameras is crucial. Periodic check-ins or mood surveys can supplement or replace camera-based emotional data. Regularly updating and training the YOLOv6 model with new data samples will enhance its accuracy and robustness under varied conditions. #### 4.2.3 Student Behaviour Acquisition

Student behaviour acquisition is a process that monitors and analyses student behaviours in an e-learning context to improve engagement classification. This involves collecting data on various student behaviours, such as attendance rate, actions (such as frequently pausing the course video), chitchatting, disturbances, and malpractice during exams. Processing these behaviours provides insights into their significance in the learning process. The Threshold-based Shannon Entropy (TSE) method is used to categorise these behaviours into 'good' and 'bad', based on their entropy values. The ultimate goal is to improve engagement classification results, allowing the e-learning system to assess student engagement levels and tailor the learning experience to enhance both engagement and academic performance. This approach demonstrates a comprehensive effort to use behavioural data to improve the effectiveness of e-learning systems. #### 4.2.4 CI Behaviours and Teaching Style Acquisition

Finally, the daily behaviours of the course instructors (CIs) and their teaching styles, including their level of interactiveness, course time management, and teaching methods (e.g., PowerPoint presentations, voice, video, etc. ), are classified into two categories, acceptable and non-acceptable, using the Threshold-based Shannon Entropy (TSE) method.