1. Introduction

2. Literature Review

3. Problem Definition

4. Proposed Methods

Figure 1. Overall System Architecture.

Figure 1. Overall System Architecture.

4.1. Federated Learning-Based Authentication

The study [43] significantly expands the discussion on data privacy challenges and personalised education solutions, contributing to a richer literature review. It explores a low-latency edge computation offloading strategy for trust evaluation in finance-level Artificial Intelligence of Things (AIoT) environments. The goal is to minimise latency in data processing and decision-making by relocating these processes to edge devices closer to data sources. This approach aims to improve the speed and reliability of AI operations, thus enhancing technology-driven environments' efficiency.

Similarly, the implementation of federated learning-based authentication in e-learning enhances data privacy and security. This approach allows for the decentralised training of machine learning models across numerous devices without necessitating the sharing of sensitive data, thereby preserving user privacy [44]. It acts as a safeguard against potential data breaches by decentralising data storage and ensuring adherence to privacy regulations. Moreover, federated learning enables the delivery of personalised learning experiences. It optimises bandwidth usage, establishing it as a bandwidth-efficient methodology. By integrating these advanced techniques, the paper underlines the importance of adopting innovative solutions to overcome the prevalent challenges in e-learning platforms. Federated learning, in particular, plays a pivotal role in maintaining the confidentiality and integrity of data while facilitating a customised educational experience for learners. This synergy between cutting-edge AIoT strategies and federated learning underscores the evolving landscape of secure, personalised, and efficient e-learning environments. To mitigate malicious activities in the adaptive e-learning environment, a robust and privacy-preserving authentication scheme has been developed for both course instructors (CIs) and students, utilising Federated Learning (FL) technology. This scheme involves CIs, students, and cloud servers (CS).

The authentication process begins with the registration initiated by CIs and students, who send their requests to the CS. The CS comprises two centralised sub-servers: the Student Server (SS) and the Course Instructor Server (CIS), where students and CIs, respectively, register their credentials. The credentials, which include ID, password, face data, voice data, and keystroke dynamics, are treated as local models.

Subsequently, the sub-servers create the global model using a Cycle General Adversarial Network (CGAN). CGAN, consisting of generators (δ) and discriminators (ϵ) for each domain (features from students and course instructors), aims to translate characteristics from one domain to another.

Generators δ_1 and δ_2: convert characteristics between domains. δ_1 transforms SS into CIs, while δ_2 transforms CIs into SS.

$${\mathit{\delta }}_1\left(\alpha \right)\to \left(\beta \right)$$

(1)

$${\mathit{\delta }}_2\left(\beta \right)\to \left(\alpha \right)$$

(2)

Discriminator ${\mathit{\epsilon }}_1$ and ${\mathit{\epsilon }}_2$: evaluate the resemblance of generated features to real features in their respective domains.

For ${\mathit{\epsilon }}_1$ (Discriminating SS)

$${\mathit{\epsilon }}_1\left(\beta \right)\to \left[\mathrm{0,1}\right]$$

(3)

For ${\mathit{\epsilon }}_2$ (Discriminating CIs)

$${\mathit{\epsilon }}_2\left(\alpha \right)\to \left[\mathrm{0,1}\right]$$

(4)

Cycle consistency loss: ensures that a feature translated from one domain to another and back again should resemble the original feature, preserving information. Let's refer to the translated features as ${\alpha }^{\prime }$ (SS) and ${\beta }^{\prime }$ (CIs).

For ${\delta }_1$ (SS to CIs like) and its reverse:

$${\mathcal{L}}_{cycle\hspace{0.17em}{\delta }_1}={\left|\left|{\delta }_1\left({\delta }_2\left(\alpha \right)\right)-\alpha \right)|\right|}_1$$

(5)

For ${\delta }_2$ (CIs to SS like) and its reverse:

$${\mathcal{L}}_{cycle\hspace{0.17em}{\delta }_2}={\left|\left|{\delta }_2\left({\delta }_1\left(\beta \right)\right)-\beta \right)|\right|}_1$$

(6)

Here, ${\mathcal{L}}_{identity}$ encourages generated features to resemble the original, while ${\mathcal{L}}_{cycle}$ ensures cycle consistency, eq. (7) represents the generator loss (${\mathcal{L}}_{\mathit{\delta }})$.

$${\mathcal{L}}_{\delta }={\mathcal{L}}_{identity}\left({\delta }_1\right)+{\mathcal{L}}_{identity}\left({\delta }_2\right)+{\mathcal{L}}_{cycle}\left({\delta }_1\left({\delta }_2\right)\right)+{\mathcal{L}}_{cycle}\left({\delta }_2\left({\delta }_1\right)\right)$$

(7)

Here ${\mathcal{L}}_{real}$ encourages the discriminator to correctly identify real features, while ${\mathcal{L}}_{fake}$ encourages the discriminator to accurately identify generated features as fake. eq. (8) represents the discriminator loss (${\mathcal{L}}_{\epsilon })$.

$${\mathcal{L}}_{\epsilon }={\mathcal{L}}_{real}\left({\epsilon }_1\right)+{\mathcal{L}}_{fake}{(\epsilon }_1\left({\delta }_1\right))+{\mathcal{L}}_{real}\left({\epsilon }_2\right)+{\mathcal{L}}_{fake}({\epsilon }_2\left({\delta }_2\right))$$

(8)

The generated global models are shared with the students and CIs for authentication purposes. It's important to note that the CIs' models and student models are swapped between the sub-servers to ensure a secure and private environment. By utilising these trained models, issues commonly associated with conventional authentication methods, such as sniffing, spoofing, and shoulder surfing attacks, can be effectively mitigated through the proposed federated learning-based authentication approach.

4.2. Majority Voting-Based Multi-Objective Student Engagement Clustering

Only authenticated students and course instructors (CIs) are permitted to participate in the adaptive e-learning course. Throughout the online course, through multiple distributed interactions, Intelligent Software Agents (ISAs) are responsible for capturing students' academic behaviours, interactivity, emotions, and other relevant behaviours, as well as CIs' behaviours and teaching styles. This information is utilised to determine the weekly engagement status of the students. To be more specific, the responsibilities of ISAs are outlined below:

4.2.1. Students Interactiveness Acquisition

Data quality in an e-learning environment, especially regarding emotional data collected through cameras, is critical. Encouraging camera use while ensuring informed consent and privacy involves establishing clear guidelines on camera positioning, lighting, and background. Additionally, setting minimum technical standards for camera resolution and internet connectivity is essential. To technologically enhance data quality, implementing real-time feedback mechanisms is crucial. These mechanisms alert students when the camera is off or if the video quality is compromised due to inadequate lighting or incorrect angles. Furthermore, employing pre-processing algorithms such as brightness and contrast adjustments, noise reduction, and image stabilisation can significantly improve the clarity of facial features, ensuring a more accurate analysis of student emotions.

During online courses, course instructors (CIs) engage with students through various methods, such as posing questions, conducting seminars, and administering short quizzes related to the lectures, among other interactive activities. Based on these daily interactions, Intelligent Software Agents (ISAs) evaluate the students' levels of interactivity each week, categorising them into two classes: "highly interactive" and "less interactive." This evaluation employs the threshold-based Shannon entropy (TSE) method, which quantifies the frequency of interactions exceeding a predefined threshold, thereby assessing engagement levels.

Shannon entropy, denoted as H(ϰ), measures the uncertainty in a dataset. It is calculated for a discrete random variable ϰ with probability distribution P(ϰ) using the formula:

$$\mathrm{H}\left(\varkappa \right)=-{\sum }_x\mathrm{P}\left(\varkappa \right)·{\mathrm{l}\mathrm{o}\mathrm{g}}_2\left(\mathrm{P}\left(\varkappa \right)\right)$$

(9)

Here, x represents the possible values of ϰ, and P(ϰ) is the probability of each value occurring. TSE specifically focuses on values exceeding a specified threshold, represented as ℧. To compute TSE (H), it first establishes the probability distribution for interactions surpassing the threshold and then employs the Shannon entropy formula.

$$\mathfrak{H}=H(\varkappa >\mathrm{\mho })$$

(10)

Here, ϰ > ℧ represents the set of values exceeding the threshold value. H(ϰ > ℧) denotes the Shannon entropy for values greater than the threshold. TSE is calculated and then utilised to determine each student's interactiveness grade for the week. For instance, if TSE is below a certain threshold, the student could be categorised as "less interactive" (represented as ϱ_low), whereas if TSE is above the threshold, the student could be classified as "highly interactive" (represented as ϱ_high).

4.2.2. Students’ 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 YOLO-V6 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 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. CIs 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.

Based on the acquisition results mentioned above, the weekly engagement status of the students is computed using the Majority Voting Multi-Objective Clustering (MV-MOC) method. In this context, the multiple objectives pertain to the students' weekly academic and behavioural status, encompassing interactiveness, academic emotions, and behaviours, respectively.

Multi-Objective Optimisation: MV-MOC extends conventional clustering by optimising multiple objectives concurrently. Each objective corresponds to a distinct clustering solution, and the algorithm aims to find a compromise among these objectives.

Pareto Front: The Pareto Front is a set of solutions that are not dominated. A solution is considered non-dominant if it cannot be improved on one objective without negatively impacting another. The Pareto front illustrates the trade-offs between these objectives.

Majority Voting: MV-MOC assigns data points to clusters based on the consensus of multiple clustering solutions using majority voting. The diversity between Solutions X and Y, based on their cluster assignments, can be calculated. This can be achieved using the Jaccard Index (J), which measures the dissimilarity or diversity between two sets, X and Y.

$$\mathrm{J} (X,Y)=\frac{X\cap Y}{X\cup Y}$$

(11)

Here, $X\cap Y$ represents the number of data points assigned to the same cluster in both Solution $X$ and Solution $Y$ (the intersection of sets $X$ and $Y$), while $X\cap Y$ represents the total number of unique data points assigned to clusters in Solutions $X$ and $Y$ (the union of sets $X$ and $Y$).

The Pareto Dominance Test is performed to determine whether Solution $X$ dominates Solution $Y$ based on multiple objectives. Solution $X$ is considered to dominate Solution $Y$ if it is equal to or better than Solution $Y$ in all objectives. This dominance check can be expressed for each objective $i$ as follows:

$$f_i(S_X)\le f_i(S_Y)$$

(12)

Here, $f_i\left(S_X\right)$ represents the value of objective $i$ in Solution $X$, and $f_i\left(S_Y\right)$ represents the value of objective $i$ in Solution $Y$. For Solution $X$ to dominate Solution $Y$, this inequality must hold for all objectives $i$. If it holds for at least one objective, Solution $X$ is considered to dominate or be equivalent for some objectives, but not all.

In summary, these equations are used to analyse the diversity between two clustering solutions (Diversity Calculation) and to check if one solution dominates another based on multiple objectives (Pareto Dominance Check). In the final step of the MV-MOC algorithm, the number of times each data point has been assigned to each cluster across all Pareto front solutions is calculated. Subsequently, each data point is assigned to the cluster that receives the majority of votes, determining its final assignment. Optionally, the quality of the clustering solution can be evaluated using cluster validity $\mathcal{f})$ indices, such as the silhouette score, which measures how well the data points are clustered. $\hslash$ represents the Pareto front solutions.

To ensure that the algorithm does not operate indefinitely, a termination condition is imposed based on either a fixed number of iterations or convergence. Ultimately, the final clustering solution consists of clusters obtained through majority voting based on Pareto-front solutions, enabling effective classification of data points according to their engagement status or other relevant objectives. Concerning these objectives, the majority voting method clusters students into classes, such as engaged and non-engaged students. To clarify, students exhibiting good behaviour and positive academic emotions but lower levels of interactiveness would be categorised as engaged students, and vice versa.

5. Experimental Results

5.1. 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-score, true positive rate, false positive rate, true negative rate, and false negative rate.

5.1.1. No of Epochs vs Accuracy

Certainly, a simple linear equation can be used to represent the link between the number of training epochs and accuracy (∀):

$$\forall =\mathcal{\wp }.\mathcal{E}+\mathfrak{I}$$

(17)

Here, $\mathcal{E}$ represents the number of training epochs, and the rate of change in accuracy per epoch is given by ℘. It indicates how much accuracy increases with each additional epoch. $\mathfrak{I}$ is the starting accuracy, often known as the intercept, which represents the accuracy at epoch 0. Figure 2 and Table 2 presents the number of epochs vs. accuracy.

As the number of epochs increases, the accuracy rate also increases. Choosing 5 epochs as a comparison point in machine learning model training can indicate early performance, assess model efficiency, and benchmark against standard practices. It also highlights resource efficiency, as fewer epochs mean less computational time and energy usage. For example, in several epochs (e.g., 5), the accuracy of the proposed method reaches 65%, while existing methods such as PCT achieve 15%, MST-FaDe achieve 25%, Fuzzy SVM achieve 35%, and AISAR achieve 45%. With a higher number of epochs (e.g., 25), the proposed method attains an accuracy level of 73%, while PCT reaches 39%, MST-FaDe reaches 49%, Fuzzy SVM reaches 59%, and AISAR reaches 61%. Ultimately, the proposed method demonstrates a higher accuracy level compared to existing ones.

Figure 2. Number of Epochs vs Accuracy.

Figure 2. Number of Epochs vs Accuracy.

5.1.2. No of Epochs vs F1-Score

Certainly, the relationship between the number of training epochs and the F1-Score can be succinctly explained through a simple equation. The F1-Score is a widely used metric for evaluating the balance between precision and recall in classification problems. The formula is traditionally expressed as:

$$\mathfrak{F}=\frac{2.(\mathfrak{E}.\mathfrak{K})}{\mathfrak{E}+\mathfrak{K}}$$

(18)

The F1-Score (F) is the harmonic mean of precision (E) and recall (K). Precision (E) is defined as the ratio of true positives to the sum of true positives and false positives, while recall (K) is defined as the ratio of true positives to the sum of true positives and false negatives. Consequently, the F1-Score provides a single value that encapsulates model performance, considering both false positives and false negatives. This offers a balanced perspective, particularly useful in scenarios with uneven class distributions.

Table 3 represents numerical outcomes, and Figure 3 illustrates the relationship between the number of epochs and the F1-score. As the number of epochs increases, the F1-score correspondingly rises. In the proposed method, F1-scores of 50%, 65%, and 95% are achieved with 5, 25, and 50 epochs, respectively. Conversely, the PCT method yields F1-scores of 42%, 52%, and 76%; the MST-FaDe method yields F1-scores of 43%, 57%, and 79%; and the fuzzy SVM method yields F1-scores of 45%, 62%, and 80% for the same respective epochs. Meanwhile, AISAR registers F1-scores of 46%, 63%, and 81% for the corresponding epochs. Existing approaches demonstrate lower F1-scores when compared to the proposed method.

5.1.3. Precision vs Recall Rate

A single equation can succinctly represent the relationship between precision and recall as:

$$\mathfrak{E}=\frac{\mathfrak{k}}{\mathfrak{k}+\mathfrak{r}}$$

(19)

Here, precision $(\mathfrak{E}$) is a metric representing the accuracy of a classification model, defined as the ratio of true positives ($\mathfrak{k}$) to the total predicted positives ($\mathfrak{k}$ + $\mathfrak{r}$). True positives ($\mathfrak{k}$) refer to instances correctly identified as positive by the model, while false positives ($\mathfrak{r}$) denote instances that are incorrectly labelled as positive.

In contrast, $\mathfrak{K}$ can be expressed using a different equation:

$$\mathfrak{K}=\frac{\mathfrak{k}}{\mathfrak{k}+\mathfrak{Z}}$$

(20)

Here, recall $\mathfrak{K}$ of a classification model is represented as the ratio of true positives to total actual positives ($\mathfrak{k}+\mathfrak{Z}$). False negatives ($\mathfrak{Z}$) are instances that the model fails to identify as positive. Table 4 illustrates the numerical outcomes and Figure 4 illustrates the relationship between precision and recall.

PCT initiates with a low precision of 0.08, gradually improving precision as the recall rate rises, reaching an accuracy of 0.85 and a recall rate of 1.0, MST-FaDe initiates with a low precision of 0.10, increasing accuracy over time while maintaining a constant recall rate, reaching an accuracy of 0.90 and a recall rate of 1.0, Fuzzy SVM initiates with a low precision of 0.11, steadily enhancing precision as the recall rate ascends, achieving an accuracy of 0.95 and a recall rate of 1.0. AISAR begins with a reasonable precision of 0.12, which rapidly improves alongside the recall rate, attaining a precision of 0.96 at a recall rate of 1.0. The proposed model demonstrates the highest precision among the five models, commencing at 0.13 and progressively improving, culminating in a precision of 0.99 at a recall rate of 1.0.

5.1.4. True Negative Rate vs False Negative Rate

The true negative rate ($\mathcal{A}$) and false negative rate ($\mathcal{B}$) are pivotal metrics used in binary classification to assess a classifier's performance. Typically, these rates are computed using the confusion matrix, which is a table that summarises the classifier's predictions. $\mathcal{A}$, Specificity, represents the fraction of actual negatives (class 0) that the classifier correctly predicts as negatives. Here is the $\mathcal{A}$ / $\mathcal{B}$ equation, along with a brief explanation:

$$\mathcal{A}=\frac{\mathcal{Z}}{\mathcal{Z}+\mathfrak{r}}$$

(21)

$\mathcal{B}$ represents the proportion of true negatives (class 0) that the classifier incorrectly predicts as positives and can be calculated using the following equation:

$$\mathcal{B}=\frac{\mathfrak{r}}{\mathfrak{r}+\mathfrak{k}}$$

(22)

At a false negative rate (FNR) of 0.1, PCT has a true negative rate (TNR) of 50 (1–0.50) and gradually enhances its TNR as the FNR increases, reaching 0.05 at a FNR of 1.0. MST-FaDe has a true negative rate (TNR) of 49 (1–0.51) at a false negative rate (FNR) of 0.1. Its TNR steadily improves as the FNR rises, reaching 0.05 with a false negative rate of 1.0 at an FNR of 0.1. Fuzzy SVM has a TNR of 0.48 (1–0.52) and gradually enhances its TNR as the FNR increases, reaching 0.05 at FNR 1.0. AISAR exhibits a similar pattern, beginning with a slightly higher TNR of 0.47 (1-0.53) at FNR 0.1 and improving to 0.04 at FNR 1.0.

The proposed model starts with the highest initial TNR of 0.46 (1-0.54) at FNR 0.1 and consistently outperforms the other models, achieving the highest TNR of 0.01 at FNR 1.0. Table 5 represents the numerical results and Figure 5 illustrates the trade-off between TNR and FNR for each of the five models, demonstrating that the proposed model optimally balances minimising false negatives and maximising true negatives.

5.1.5. True Positive Rate vs False Positive Rate

The true positive rate ($\mathfrak{A}$), also recognised as sensitivity or recall, and the false positive rate ($\mathfrak{s}$) are critical metrics in binary classification utilised to evaluate a classifier's performance. Both rates can be derived using the confusion matrix.

$\mathfrak{A}$ signifies the proportion of actual positives (class 1) that are accurately predicted as positives, as detailed in Eq. (20). On the other hand, $\mathfrak{s}$ represents the proportion of actual negatives (class 0) that the classifier erroneously predicts as positives. The relationship for s can be described as:

$$\mathfrak{s}=\frac{\mathfrak{k}}{\mathfrak{k}+\mathfrak{r}}$$

(23)

In practical terms, PCT starts with a true positive rate (TPR) of 0.45 and gradually elevates the TPR with a rising false positive rate (FPR), achieving 0.77 at FPR 1.0. The true positive rate (TPR) of MST-FaDe is initially set at 0.49, and it is progressively increased with an increasing false positive rate (FPR) to reach 0.84 at FPR 1.0. Fuzzy SVM starts with a TPR of 0.52 and gradually elevates the TPR with a rising FPR, achieving 0.90 at FPR 1.0. AISAR exhibits a similar trajectory, initiating with a TPR of 0.53 at an FPR of 0.1 and escalating to 0.91 at an FPR of 1.0. The proposed model begins with a TPR of 0.54 at an FPR of 0.1, consistently outperforming the prior models, and achieves the highest TPR of 0.98 at an FPR of 1.0. Table 6 represents the numerical outcomes, and Figure 6 illustrates the trade-off between TPR and FPR for all models, demonstrating that the proposed model consistently sustains a higher TPR while managing FPR, indicative of enhanced classification task performance.

5.1.6. Specificity vs Number of Epochs

A statistic called the specificity metric is used in classification tasks to assess a model's accuracy in identifying negative situations. It is expressed as the ratio of true negatives ($\mathfrak{N}$) to the total of false positives ($\mathfrak{F}\mathfrak{N}$) and true negatives ($\mathfrak{N}$). The specificity formula is as follows in mathematics:

$$\mathfrak{S}=\frac{\mathfrak{T}\mathfrak{N}}{\mathfrak{T}\mathfrak{N}+\mathfrak{F}\mathfrak{N}}$$

(24)

$\mathfrak{T}\mathfrak{N}$ stands for True Negatives, or the quantity of negative situations that are accurately detected. $\mathfrak{F}\mathfrak{N}$ stands for False Positives, or the quantity of positive instances that were mistakenly recognised.

Figure 7 displays the specificity, while Table 7 shows the specificity's numerical results. When compared to other current techniques like PCT, MST-FaDe, fuzzy SVM, and AISAR, the recommended approach provides exceptional specificity. That is the overall utility as the number of epochs increases. The greatest specificity of the suggested methods is 98% in terms of epochs; by comparison, the maximum specificities of PCT, MST-FaDe, fuzzy SVM, and AISAR are 70, 80, 85, and 89 bits per second, respectively. Our suggested strategy outperforms the current ones in terms of specificity.

6. Conclusions

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