## 3 Background
### AdaBoost Classification Techniques
The sigmoid function then converts these values into a range between 0 and 1. This transformation allows the gate to filter information, distinguishing between less important and more critical information for future steps. Equation (3) represents the current memory content during the training process, whereas Equation (4) depicts the final output in the memory at the current time step. \[h^{{}^{\prime}}_{t}=\text{t}\,ant\,\left(\text{W}_{x_{t}}+\text{r}_{t}\ \odot\ Uh_{t-1}\right) \tag{3}\]  
\[h_{t}=\text{r}\ \odot\ \Big{(}1-gate_{update}\Big{)}+u \tag{4}\]  
The proposed GRU model simplifies the understanding of how sequential data inputs impact the final sequence generated as the model's output. This capability is key in unraveling the internal operational mechanisms of the model and fine-tuning specific input-output correlations. Additionally, experimental evaluations using students' internal assessment datasets have demonstrated that the GRU model surpasses the performance of traditional models. Deep learning models often train with noisy data, necessitating the use of specialized stochastic optimization methods like the ADAM algorithm [37]. Renowned for its effectiveness in deep learning contexts, the ADAM algorithm is favored for its ease of implementation and low memory requirements, contributing to computational efficiency. It is particularly adept at handling large datasets and numerous parameters. The ADAM algorithm combines elements of stochastic gradient descent and root mean square propagation, incorporating adaptive gradients. During training, it utilizes a randomly selected data subset, rather than the entire dataset, to calculate the actual gradient. This approach is reflected in the workings of the algorithm, as detailed in Equations (5) and (6) [38]:  
\[mt=\beta 1\ m_{t-1}+(1-\beta 1)t \tag{5}\]  
Figure 4: The architecture of a Gated Recurrent Unit (GRU). \[v\mathrm{t}=\beta 2/v_{t-1}+(1-\beta 2)\mathrm{t}2 \tag{6}\]  
where \(m_{t}\) and \(v_{t}\) must approximate the instant of the gradients, where they are adjusted as vectors of 0's, and \(\beta 1\) and \(\beta 2\) are close to zero. These biases are emulated through the computation of bias-adjusted moment estimations, as delineated in Equations (7)-(9). \[\mathrm{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{7}\]  
\[v_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{8}\]  
Subsequently, the update rule is applied. \[\text{gate}_{\text{update}}= \ \sigma\Big{(}\text{W}_{\text{input}_{\text{update}}}.\,\text{x}_{ \text{t}}+\text{W}_{\text{hidden}_{\text{update}}}.\,\text{h}_{\text{t}-1} \Big{)} \tag{1}\]0  
The default values are as follows: \(\beta 1=0.9\), \(\beta 2=0.999\), and \(\in=10^{-8}\). The proposed model includes a max-pooling layer, which serves to reduce the number of coefficients in the feature map for processing. This layer facilitates the development of spatial filter hierarchies by generating successive convolutional layers with increasingly larger windows relative to the original input's proportion [39]. Furthermore, the proposed GRU model incorporates a dense layer. This layer is fully connected to its preceding layer, meaning every neuron in the dense layer is linked to every neuron in the layer before it. The dense layer receives outputs from all neurons of its preceding layer and performs matrix-vector multiplication. In this context, the matrix's row vector, representing the output from the preceding layer, corresponds to the column vector of the dense layer [40]. Figure 5 visually presents the primary configurations of the proposed GRU model, while Figure 5 depicts the model's development process. The importance of utilizing GRU-powered recommendation systems within educational frameworks lies in their profound ability to discern students facing academic difficulties. These advanced systems, through their analytical prowess, are essential for educational institutions aiming to proactively identify and support students who are not achieving their full academic potential.