## 1 Introduction  
The increasing integration of digital technologies into educational environments has substantially expanded the volume and diversity of data available for analysis, creating a need for advanced analytical techniques capable of revealing meaningful insights. In response, Educational Data Mining (EDM) has emerged as a research area that applies data-driven methods to identify learning patterns, predict student outcomes, support adaptive tutoring systems, and inform curriculum development [1, 2]. Based on this research, [PERSON] et al. [3] investigated the integration of data mining methods into Learning Management Systems (LMSs), such as Moodle, and demonstrated their potential to enhance personalized learning and instructional support. In parallel, the implementation of artificial intelligence (AI) tools, including chatbots, has increased in educational environments, with research indicating enhancements in student participation and academic achievement [4]. A challenge in this area is that academic tools frequently produce large volumes of data without corresponding outcome labels. Data annotation is time-consuming and expensive because it requires manual input from educators or domain specialists, as noted in [5]. This constraint limits the applicability of traditional supervised learning methods and motivates the use of alternative approaches, such as semi-supervised learning (SSL), which can leverage both labeled and unlabeled data to improve model performance. SSL allows models to extract underlying patterns from unlabeled data, which reduces the need for extensive manual annotation [6]. Moreover, this approach is particularly well suited to educational environments where labeled examples are frequently in short supply [7]. In SSL-based scenarios, feature selection (FS) plays a critical role in identifying the most informative variables and excluding variables that add limited value to the prediction process. FS can improve model interpretability, reduce its complexity, and enhance classification performance by shrinking the dimension of the feature space [8; 9]. However, this process may also omit variables that, although weakly informative in isolation, could contribute to meaningful interactions when combined with others. Therefore, important patterns may be overlooked, potentially limiting the model's capacity to capture complex relationships in educational data. This trade-off motivates the exploration of approaches that can learn complex dependencies directly from raw data, such as Deep Learning (DL). Recent advances in DL have demonstrated the potential to learn hierarchical representations automatically from raw data [10]. Deep neural networks (DNNs), a category of artificial neural networks (ANNs), are particularly effective in modeling complex and non-linear relationships, making them suitable for analyzing detailed educational datasets [11]. Moreover, unlike traditional FS methods, ANN architectures can autonomously identify the most relevant features through backpropagation and optimization [12]. Nevertheless, designing and optimizing DL models remains a challenging task that requires expertise in hyperparameter tuning, architecture selection, and feature engineering. In response to these challenges, the development of Automated Machine Learning (AutoML) aims to simplify the process of building ML models by automating tasks. Traditionally, ML and DL workflows require extensive expertise and manual efforts, making them less accessible to researchers with limited modeling engineering backgrounds. To overcome this limitation, AutoML frameworks explore a range of algorithms and configurations, often employing techniques such as genetic algorithms and neural architecture search [13; 14]. Specifically, in the context of EDM, AutoML can enhance predictive modeling by optimizing feature representations and adapting to the complexity of learning behaviors, thereby improving decision-making processes in adaptive learning environments [15; 16].