## 4 Results
### Future Directions of Research
In the educational area, multimodal learning analytics allows for a more complete and detailed profile of how students learn, therefore providing a more comprehensive perspective of their needs and behaviors [62]. With the support of AI, multimodal learning analytics allows for more sophisticated and accurate analysis of these multiple data sources. AI and ML algorithms process the vast amounts of unstructured and multimodal data (such as video or behavioral records) to identify behavioral patterns that would not otherwise be perceptible. For example, AI can detect signs of disengagement, confusion, or frustration from facial expressions, tone of voice, or interaction patterns on digital platforms, allowing teachers to adjust their strategies in real time to improve the learning experience [63]. In education, this approach offers multiple applications. At the classroom level, multimodal learning analytics can help personalize instruction by providing real-time information on each student's understanding and engagement. On online learning platforms, this technology can be used to adjust content and activities based on each user's style and pace, fostering more personalized and adaptive learning [64]. One future line of research, supported by AI, aims to improve the accuracy and applicability of educational interventions and develop ethical and effective analysis methods. These studies can help create a more inclusive and equitable educational environment by understanding and addressing the diverse ways in which students learn, driving truly student-centered education. Latent Dirichlet allocation (LDA) is a statistical modeling technique primarily used for topic analysis in large collections of text. In LDA, texts are considered as a mixture of latent topics, where each word in the document has a probability of belonging to a specific topic. This model makes it possible to automatically identify underlying topics in large volumes of textual data, such as academic articles, social media posts, or survey responses, therefore providing an organized structure for analyzing large amounts of unstructured information [65]. In education, LDA, supported by AI, has significant potential to improve the understanding of patterns and trends in textual educational data. For example, LDA can analyze open-ended student responses in assessments, comments in online learning forums, or even student-generated content on educational platforms. By identifying key themes in these texts, LDA helps educators and administrators gain deeper insight into students' concerns, ideas, and needs. Integrated into AI systems, LDA also facilitates the personalization of learning by automatically classifying educational content, such as books or articles, based on specific topics relevant to each student or group of students [66]. In this way, AI algorithms can recommend study materials tailored to each student's learning style and preferences, therefore optimizing the relevance and effectiveness of educational content. Future research in LDA applied to education can focus on improving the accuracy and interpretability of topic analysis, allowing teachers and adaptive learning systems to better understand students' motivations and difficulties. It is also expected that, when combined with advanced AI and deep-learning techniques, LDA will become an increasingly powerful tool for analyzing learning patterns and offering personalized educational experiences [67]. This would drive the creation of more inclusive, tailored, and dynamic educational environments, helping students engage in their learning process in a more meaningful way tailored to their interests. * Source Code Vulnerability Detection  
Source code vulnerability detection is increasingly integrated into education, especially in computer science, software development, and cybersecurity programs. Including this knowledge in the curriculum allows students to identify and prevent security risks in the software they develop, promoting more secure and reliable applications.