# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods
### Federated Learning-Based Authentication
This VLE-based dataset includes a wide array of data, featuring information on 32,593 students across 22 courses, coupled with detailed records of click activities totalling 10,655,280 entries. The OULAD-VLE dataset is extensive, encompassing three distinct types of data: demographics, performance, and learning behaviour. The dataset features a range of information categories, including course details, student demographics, registration data, assessments, VLE interactions, student results, and student assessment scores. Course information comprises the code name, presentation duration, and semester offering. Demographic details cover student identification numbers, regional locations, and age groups. Registration data reveal enrolment patterns and dropout rates. The assessment section offers a comprehensive view of each course's evaluation methods, including the type of assessment, its weight, and the students' final scores. Logs of VLE interactions provide an in-depth look at student engagement with the online platform, which is crucial for measuring engagement levels and understanding how they correlate with learning outcomes. Student results detail the final outcomes for each course attempt, facilitating analyses against demographic and engagement metrics. Lastly,student assessment scores give a clearer picture of individual performances across different assessment types and stages within each course. The OULAD enables in-depth learning analytics studies focused on identifying factors contributing to student success and developing predictive models to improve educational outcomes. Researchers can use this dataset to explore relationships between student engagement, assessment performance, demographic factors, and final results, enhancing their understanding of effective online learning practices. This work utilised a variety of data types to assess the model's effectiveness. Student demographic data (e.g., age, gender, nationality, and socio-economic status) were used to assess the model's generalisability across diverse populations, and academic history data (grades, test scores, and assignment completion rates) were analysed to understand student performance patterns and trends. Engagement metrics (login frequency, time spent on materials, forum participation, and quiz/assignment completion) measured student interaction with the e-learning platform. For instance, data might show that students who log in daily and actively participate in forms tend to have higher academic performance. Additionally, multi-modal biometric data (e.g., facial expressions, eye movements, and keystroke dynamics) [data reflecting physiological and behavioural responses] were captured to identify emotional states, like confusion or disengagement, triggering support mechanisms. For example, a student's frequent eye movements away from the screen might indicate a lack of focus. Behavioural data (number of video pauses, replays, and multitasking instances) provided insights into students' learning behaviours. Frequent pausing of lecture videos, for instance, might indicate difficulty understanding the material. By incorporating these diverse types of data, our evaluation process demonstrates how the DRL-based early warning system can accurately predict student performance and provide personalised recommendations to enhance their academic success. This comprehensive data approach ensures a robust evaluation and validation of our model's effectiveness. ### Comparative Analysis

This section compares the proposed method with several existing ones, including PCT [43], MST-FaDe [37], Fuzzy SVM [29], and Artificial Intelligence-Based Student Assessment and Recommendation (AISAR) [38]. It evaluates its effectiveness using performance metrics, such as accuracy, precision, recall, F1-scores, true positive rates, false positive rates, true negative rates, and false negative rates. #### 5.1.1 No. of Epochs vs.