## 4 Experiments
### Characteristic of the Convolutional Neural Network Model  
The second experiment explored whether the proposed convolutional deep neural network models (i.e., CNN-LSTM and ConvLSTM) could achieve better than baseline NN models (i.e., LSTM) for weight convergence in feedforward neural networks. In the LSTM approach, the 128 vector lengths with absolute values were constructed into one-dimensional data for data recognition to identify at-risk students. On the other hand, in both the CNN-LSTM and the ConvLSTM approaches, the 128 vector lengths were composed into two-dimensional 16 \(\times\) 8 data to improve spatial feature recognition between adjacent data values. An LSTM model architecture with an LSTM layer as feature extraction and a fully connected network (FCN) layer as a prediction model enabled the architecture to learn more complex long short-term representations for classification tasks. Each layer propagated the output of the previous layer as its input, providing comprehensive learning by training the weights to reach a convergence state. A dropout layer was used between HLs to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instance** & **NSD** & **NM** & **Skewness** & **Kurtosis** \\ \hline Assignment & 0.071788 & 0.016286 & 3.655578 & 16.921989 \\ File & 0.049630 & 0.006317 & 7.398770 & 70.913583 \\ Forum & 0.056196 & 0.016364 & 14.388217 & 247.806282 \\ Homepage & 0.114323 & 0.042051 & 9.775106 & 136.711158 \\ Label & 0.068407 & 0.011654 & 7.995445 & 78.579698 \\ Page & 0.080140 & 0.011738 & 8.452196 & 79.764197 \\ Quiz & 0.055256 & 0.006360 & 11.052219 & 143.488354 \\ URL & 0.054987 & 0.006173 & 12.353567 & 179.174638 \\ Class label (target) & 0.332847 & 0.873503 & \(-\)2.246093 & 3.046236 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The evaluated data sets and their characteristics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **Classification** & **Model** & **Oversampling** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline None & LR & None & 0.920 & 0.775 & 0.955 & 0.830 \\  & hybrid SMOTE & 0.910 & 0.900 & 0.915 & 0.910 \\ \hline None & SVM & None & 0.930 & 0.840 & 0.840 & 0.840 \\  & hybrid SMOTE & 0.930 & 0.925 & 0.935 & 0.930 \\ \hline LSTM & FCN & None & 0.938 & 0.855 & 0.845 & 0.845 \\  & hybrid SMOTE & 0.934 & 0.948 & 0.930 & 0.930 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the baseline model’s performance using a 16-week-wise data set. avoid interdependency between the LSTM and Dense layers, reducing overfitting and underfitting during the training process. In the training phase, the data were computed at each epoch using several scenarios, i.e., from the first week to \(i\)-th week, where \(i=6\), 8, 10, 12, and 16. The week-wise sequences were converted to equal-length normalized input vectors. Then the vectors were padded into the input layer. A fixed Adam optimizer was applied with a dropout rate of 0.2 and categorical cross-entropy as the loss function. Multiple evaluations were performed to discover the optimal epoch, batch size, and learning rate, ranging from 10 to 300, 10 to 100, and 0.01 to 0.0001, respectively. The hyper parameter setting was decided after several experiments with the final epoch \(e=200\), the batch size \(b=20\), and the learning rate \(\alpha=0.001\). The results in Figure 3 illustrate the efficacy of the proposed (a) LSTM, (b) CNN-LSTM, and (c) Conv-LSTM models by representing the training improvement after 200 epochs. The figure presents three kinds of neural network models, i.e., LSTM, CNN-LSTM, and Conv-LSTM, to predict students' performance. Generally, on the left side of Figure 3, the loss accuracy progressively decreases from the early weeks to the late weeks. In contrast, on the right side of Figure 3, the accuracy progressively increases from the initial weeks to the final weeks.