The rapid expansion of virtual education has highlighted both its opportunities and limitations. Conventional virtual learning environments tend to lack flexibility, often applying standardized methods that do not account for individual learning differences. In contrast, Artificial Intelligence (AI) empowers the creation of customized educational experiences that address specific student needs. Such personalization is essential to mitigate educational inequalities, particularly in areas with limited infrastructure, scarce access to trained educators, and varying levels of digital literacy. This study explores the role of AI in advancing virtual education, with particular emphasis on supporting differentiated learning. It begins by selecting an appropriate pedagogical model to guide personalization strategies and proceeds to investigate the application of AI techniques across three key areas: the characterization of educational resources, the detection of learning styles, and the recommendation of tailored content. The primary contribution of this research is the development of a scalable framework that can be adapted to a variety of educational contexts, with the goal of enhancing the effectiveness and personalization of virtual learning environments through AI.

Researchers have extensively explored learning analytics in online courses, primarily focusing on linear course structures where students progress sequentially through lessons and assessments. However, non-linear courses, which allow students to complete tasks in any order, present unique challenges for learning analytics due to the variability in course progression among students. This study proposes a method for applying learning analytics to non-linear, self-paced MOOC-style courses, addressing early performance prediction and online learning pattern detection. The novelty of our approach lies in introducing a personalized feature aggregation that adapts to each student’s progress rather than being defined at fixed timelines. We evaluated three types of features—engagement, behavior, and performance—using data from a non-linear large-scale Moodle course designed to prepare high school students for a public university entrance exam. Our approach predicted early student performance, achieving an F1-score of 0.73 at a 20% cumulative weight assessment. Feature importance analysis revealed that performance and behavior were the strongest predictors, while engagement features, such as time spent on educational resources, also played a significant role. In addition to performance prediction, we conducted a clustering analysis that identified four distinct online learning patterns recurring across various cumulative weight assessments. These patterns provide valuable insights into student behavior and performance and have practical implications, enabling educators to deliver more personalized feedback and targeted interventions to meet individual student needs.

Obfuscated and fileless malware families evade traditional detection systems by residing exclusively in memory and employing stealthy techniques such as process injection and encrypted communication. Although memory-based detection methods have demonstrated strong performance using host-based features alone, the contribution of network-level information remains underexplored. This study addresses this gap by leveraging the recently released WinMal25 dataset, which comprises approximately 2 TB of ground-truth Windows memory dumps collected under realistic benign activity and obfuscated malicious execution. We extract a small set of socket- and connection-level variables directly from RAM and evaluate their contribution to malware detection using Random Forest and XGBoost classifiers under multiple feature configurations. The experimental results show that network-related structures preserved in memory are highly discriminative on their own and further enhance detection performance when combined with traditional system-level features. These findings demonstrate that communication-related structures preserved in memory constitute a robust and complementary forensic signal, supporting the development of interpretable and generalizable memory-based malware detection systems capable of operating under heavy obfuscation.

Although student performance prediction in online courses has been extensively studied before, previous research efforts have focused mostly on courses with a linear structure, where the student is expected to take the lessons and assessments in a sequential (linear) and unique order. There are, however, non-linear courses where the student can take the lessons and assessments in any order they wish, which makes the performance prediction more challenging, as the cumulative assessment percentage might vary widely between students at a given point in time. Here, we present a data-driven method to predict student performance in non-linear courses. In particular, our method predicts whether or not a student will reach a final g rade a bove a fi xed th reshold fo r di fferent percentages of assessment completion. We use data from a Moodle course designed to prepare high-school students for the entrance exam of a public university. We show that for cumulative assessment percentages ≥60%, our method has a mean F1-score above 70%. Finally, we also assess the importance of each feature used in the prediction, illustrating the effect of our data preprocessing and feature selection approach on the model performance. Our future research efforts will focus on applying our predictive models in real-world scenarios, particularly within educational settings where learning resources dynamically adapt based on student performance.