# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods
### Federated Learning-Based Authentication
### DRL-Based Early Warning System & Multi-Disciplinary Ontology Graph Construction

The Improved Duelling Deep Q Network (ID2 QN) serves as an early warning system employing a deep reinforcement learning (DRL) model to forecast student performance. This prediction is based on their engagement status, demographic details, and other pertinent data. Designed to anticipate student performance early, the ID2 QN aims to identify students requiring additional support or resources for success. Unlike traditional DRL methods that rely on a table for storing Q-value data, which becomes impractically intensive with growing environmental complexity, ID2 QN utilises a neural network for direct state-to-action mapping, negating the need for a Q-value table. The ID2 QN algorithm distinguishes between two distinct value functions: the state value function and the action advantage function. The integration of these functions facilitates the determination of the Q-value, assisting in the selection of the most suitable action. The model adopts a dual neural network structure; one network is responsible for generating Q-values for the present state-action pair, while another calculates Q-values for future state-action scenarios. These networks' parameters are adjusted through a loss function and the stochastic gradient descent algorithm to minimise discrepancies between anticipated and actual Q-values. ID2 QN stands out for its capability to efficiently learn the value of each state without the need to explicitly determine the most advantageous action at every juncture. This efficiency renders it a potent model for early performance prediction, particularly within the realm of student engagement and success, especially in environments where accurately assessing the value of actions and states poses significant computational challenges. The workflow of the Deep Reinforcement Learning System using ID2 QN for Early Performance Prediction is as follows:

* _Data Input and Pre-processing:_

Input: Collect weekly engagement data, students' demographic details, and course instructors' (CIs) behaviours and teaching styles alongside students' academic histories for input into the neural network. Process: these inputs are utilised for the early prediction of performance among students showing signs of disengagement. * _Neural Network Structure and Q-value Estimation:_

Implement the DQN algorithm within a neural network framework, formalised by the equation:

\[\delta_{2}(\beta) \rightarrow (\alpha) \tag{2}\]2

Here, \(\psi\) represents the state, \(\varpi\) denotes potential educational interventions (actions), and \(\varphi\) embodies the network parameters. The strategy eradicates the need for traditional Q-value tables by facilitating state-to-action mappings directly within the neural network. Q-values, \(\varrho(\psi,\,\varpi,\,\varphi)\), are estimated to represent the expected rewards of certain actions given specific states. * _Loss Function and Parameter Updating:_

The Loss Function, \(\mathrm{L}(\varphi)\), assesses the gap between predicted and actual rewards, guiding neural network training, and is defined as:

\[\delta_{2}(\beta) \rightarrow (\alpha) \tag{2}\]3

Network parameters (\(\varphi\)) are updated using the stochastic gradient descent to minimise \(\mathrm{L}(\varphi)\), thereby refining prediction capabilities. * _Dwelling Network Architecture:_ ID2 QN adopts a dual-component structure: _Evaluate Net:_ Generates current state-action Q-values, \(\varrho(\psi,\,\varpi,\,\varphi)\). _Target Net:_ Outputs future state-action Q-values, \(\varrho(\psi^{\prime},\,\varpi^{\prime},\,\varphi^{\prime})\). The neural network output divides into two distinct functions: the state-value function \(\mathrm{V}(\psi,\,\varphi)\) and the action-advantage function \(\mathrm{A}(\varpi,\,\varphi)\), integrated as: \[\delta_{2}(\beta) \rightarrow (\alpha) \tag{2}\]4 (15) This delineation enhances decision-making by independently evaluating the current state's value from the advantages of the specific actions.