This research focused on determining the impact of using gamified virtual reality environments on writing skills development in university settings. The aim was to implement new teaching methods that promote meaningful learning based on digital competencies and motivation. Methodologically, the study employed Pedagogical Action Research, which involved deconstruction, reconstruction, and validation phases. The population consisted of 278 first-year students enrolled in the Language and Communication course at a private university in southern Lima. The results demonstrated the effectiveness of the implemented strategies in enhancing student motivation, primarily due to the activation of dopamine, a neurotransmitter responsible for driving action towards efficiently solving challenges. A significant finding was the promotion of autonomous learning, as the prior review of resources facilitated the resolution of challenges. Consequently, Flipped Learning effectively prepared students for hybrid classes and encouraged active apprentices. The study concludes that the use of gamified virtual reality environments positively impacts the development of students' writing skills. Motivated students showed greater willingness to practice and refine their abilities, which in turn led to an improvement in the quality of their writing.

This work discloses the results of an investigation whose objective was to determine the incidences of self-regulation in the process of writing argumentative texts in university students. The sample consisted of 20 students, aged between 16 and 18 years, drawn from a conglomerate of 40 students from the first cycle of the CRT1 course. The analysis of the information confirmed, regarding the process of written composition, that the students did not fully apply the underlying cognitive skills in such process, while writing was perceived as an academic event, whose recipient was always the teacher. Regarding the text prepared by the students, deficiencies were observed regarding the argumentative superstructure, text properties and writing regulations. After the mediation, with a learning project from a strategic teaching approach, there were considerable advances with respect to the initial situation -pretest; This proved that self-regulation positively affects both the written composition of argumentative texts and the quality of the texts produced by students. In addition, it was found that indicators of self-regulated learning underlie written composition.

This study examines digital transformation strategies in small and medium-sized enterprises (SMEs), aiming to assess their impact on competitiveness and operational efficiency. Using a systematic literature review (SLR) based on the PRISMA methodology, 40 relevant studies published between 2019 and 2024 were reviewed, including scientific articles, conferences, and reviews. The findings indicate that digital transformation helps optimize processes, reduce costs, and enhance global market positioning. Technologies such as artificial intelligence, machine learning, and automation are identified as essential drivers of this progress. However, significant barriers-such as limited financial resources, lack of technical skills, and resistance to organizational change-hinder widespread adoption. The study concludes that digitalization offers a vital opportunity to improve the sustainability and competitiveness of SMEs. It also emphasizes the need for tailored public policies and support strategies to facilitate this transition. The research aims to serve as a practical guide for business leaders and policymakers interested in advancing digital transformation within this critical economic sector.

This study aims to analyze the use of machine learning for detecting and preventing malware attacks in key technological environments such as IoT, enterprise networks, and critical systems. A comprehensive review of deep learning techniques and models applied in cybersecurity is conducted, evaluating their effectiveness, accuracy, and the limitations they face against emerging threats. The review also aims to identify the most innovative solutions that integrate artificial intelligence to enhance cyber defenses. To carry out this RSL, a methodological approach was followed, which included collecting relevant articles from academic databases, applying inclusion and exclusion criteria to ensure the quality of the selected studies. Key data was extracted and analyzed from the reviewed works, organized by the machine learning techniques used and the specific areas of application. The results showed that deep learning techniques and hybrid models have significantly improved the detection and mitigation of advanced attacks, such as ransomware and APTs. However, important challenges in implementing these technologies were identified, especially in sectors with resource limitations andresistance to organizational change. In conclusion, the use of machlearning has proven to be highly effective in improving cybersecurity, although its widespread adoption still faces barriers such as the lack of trained personnel and adequate infrastructure. This study highlights the need for further research into integrating these technologies with emerging solutions and improving their adaptability across different organizational contexts.