## 5 Conclusions  
Educational data mining serves as a powerful analytical approach for evaluating educational data and forecasting students' academic outcomes, assisting educators in making informed decisions. However, existing research faces several challenges, including the use of imbalanced datasets, reliance on entire feature sets without leveraging efficient transformation methods, and insufficient exploration of data integration strategies. To address these limitations, this research proposes an innovative architecture that combines advanced feature transformation, data integration, and ensemble learning to enhance the prediction of student academic success. In this study, we transform one-dimensional textual behavioral data into two-dimensional images using four methods: Pixel Representation (PR), Sine Wave Transformation (SWT), Recurrence Plot (RP), and Gramian Angular Field (GAF). These transformations not only enhance the visualization of behavioral features but also capture complex temporal and non-linear relationships among student behaviors. The transformed images serve as input to a CNN, which captures high-dimensional image features, while the original data is processed by an FCN to extract low-dimensional numerical features. To fully exploit the complementary nature of these heterogeneous feature representations, we propose a novel dual-stream architecture where the outputs of a CNN and FCN are concatenated at the feature level to form a unified representation. This feature-level fusion mechanism enables the model to automatically learn optimal combinations of high-dimensional and low-dimensional features through an end-to-end training pipeline. By integrating diverse feature types, the model achieves significant improvements in classification accuracy and robustness compared to single-modality approaches. Among the image transformation methods, the Recurrence Plot combined with the EnCF ensemble demonstrated the best performance, outperforming both traditional machine learning methods applied to raw data and CNN-only and FCN-only models based on image-transformed data. This highlights the effectiveness of combining neural network architectures to capture complex patterns in both raw and transformed data. The experimental findings demonstrate that the RP-based image transformation, coupled with the EnCF ensemble network, achieves the highest prediction accuracy. This integration approach leverages the strengths of both feature extraction techniques, offering a more detailed and distinct representation of student data. Compared to traditional methods, the proposed framework demonstrates superior performance in terms of classification accuracy, robustness, and interpretability. However, a significant limitation is the relatively modest dataset size, which poses challenges for the training of DL models. This limited sample size potentially elevates the risk of overfitting and may constrain the generalizability of findings to more extensive or heterogeneous datasets. Furthermore, the behavioral data employed in this study may not fully encapsulate the diversity and complexity inherent in student learning patterns across varying contexts and demographics. This limitation stems from a potential lack of comprehensiveness in the behavioral features captured. In addition, although the selected image transformation methods facilitate the extraction of salient features, they may not entirely preserve the intricate temporal and spatial relationships intrinsic to student behaviors. Consequently, some crucial predictive information for accurate modeling might inadvertently be omitted. Future studies could extend this approach by incorporating other advanced architectures, such as transformer-based models or attention mechanisms, to further improve prediction accuracy. Additionally, this method can be applied to larger and more diverse datasets to validate its generalizability across various academic performance indicators, such as course completion rates or long-term success metrics.