Students' academic performance is a key factor for educational institutions and society, which is an important indicator of the quality of the teaching-learning process and the appropriation of knowledge. Its analysis allows an understanding of the behavior of students and teachers, generating valuable knowledge for making timely academic decisions. In this study, the following phases were carried out: (i) identification of factors that influence the academic performance of engineering university students, (ii) early prediction of academic success, and (iii) identification of use patterns in a virtual learning environment (VLE). The Knowledge Discovery in Databases (KDD) methodology was applied based on predictive and descriptive data mining techniques, using academic and socioeconomic data and interactions (resources and activities) with the VLE. The tools and programming languages ​​used were Pentaho Data Integration for data integration and processing; Jupyter Notebook, Python, and Scikit-Learn for correlation analysis and prediction modeling; and R Studio for the clustering task. The results show that VLE resources such as files, links, and activities such as participation in forums are factors that lead to good academic performance. On the other hand, it was possible to make predictions of academic success (pass or not) with an accuracy greater than 95% and to identify the main patterns of use of the VLE. The group with excellent academic performance (grades 9 to 10) is recognized for using file-type resources and high participation in class and forum activities.

Computational experiments have been important since the 70s to find solutions to problems using computers, and even more now with the advancement of technology, especially artificial intelligence. The aim of this study is to know the level of interest on the part of researchers toward computational experiments in Computer Science. To find out this information, a Systematic Mapping Study has been carried out in IEEE, DBLP, Scopus, ACM and Web of Science databases. A total of 226 papers were selected as primary studies and 22 types of measurements were found, it was determined that the three most used measurements in computational experiments are performance at 51.33%, efficiency at 15.49%, and effectiveness at 15.04%. A total of 25 frameworks used for the computational experiments were determined, however 74.3% of the reports did not specify any framework used, 11.5% used a custom framework and the rest used a specific framework. It is concluded that performance is the most important measurement in computational experiments and that it is necessary for researchers to rely on a framework so that these experiments can be replicated or refined.

The integration of Large Language Models (LLMs) with web scraping and crawling techniques is transforming automated web data extraction by enabling semantic understanding and adaptability. This Systematic Literature Review (SLR) synthesizes evidence regarding this integration, focusing on tools, models, challenges, evaluation methods, trends, and applications. Following PRISMA guidelines, we conducted a rigorous search across Scopus, Web of Science, ACM, and IEEE databases (2021–2025). From 976 screened records, 91 high-quality studies (53 conference papers and 38 journal articles) were selected after duplicate removal, screening, and AI-powered quality assessment. The field has experienced explosive growth, with 84% of publications appearing in 2024–2025 alone (36 in 2024, 40 in 2025). Key tools include Scrapy, BeautifulSoup, and Selenium, with emerging LLM-augmented tools like Scrapeghost, Crawl4AI, and ScrapeGraphAI. While transformer-based models dominate (86 of 91 papers), the landscape is diversifying: the BERT family appears in 23 studies, the GPT family in 34, and other LLMs (Llama, Mistral, Claude, Gemini) in 44. Major challenges involve HTML complexity, computational costs, token limits, data biases, and legal risks. Evaluation relies on hybrid frameworks combining task-specific metrics (F1, BLEU, RAGAS), human validation, and operational efficiency measures. Applications span Cybersecurity, Healthcare, Education, E-commerce, Media, Technology, and Finance/Legal, with high thematic specialization. A notable trend is the shift toward efficient Small Language Models (SLMs) for resource-constrained, domain-specific tasks. The findings suggest that LLMs are enabling a decisive transition from rule-based to semantic, agentic approaches in web extraction. Challenges in robustness and efficiency persist, but trends point toward intelligent, domain-specialized, and ethically aware systems. Future work should explore SLM implementation, hybrid pipelines, and standardized evaluation benchmarks.

Introduction: In recent years, a significant increase in attacks targeting web applications has been observed. These attacks compromise application integrity, disrupt services, and have devastating consequences regarding data loss, reputational damage, and financial costs. Objective: The objective was to evaluate the effectiveness of the Web Application Firewall (WAF) using the OWASP methodology to detect and neutralize attacks on the Universidad Técnica del Norte’s web server. Results: The results were to categorize the main types of attacks detected by the WAF, analyze the most frequent attacks blocked by the firewall, and implement an additional layer of security on the web server. Conclusions: It was concluded that the WAF detects suspicious or potentially malicious activity in web traffic but fails to identify all cyber threats comprehensively. In addition, the WAF report, broken down each month with the number of frequent attack events identified as malicious, is a crucial tool for the web administrator.