Conference Agenda

This study presents the results of a pilot implementation of the Collective Growth System (CGS) in engineering education, contrasting the traditional transactional model with a holistic approach to human capacities development. Using a comparative research design within a Structural Analysis course, three learning trajectories (traditional, CGS, and mixed) were evaluated to analyze their impact on conceptual understanding, professional identity, and ethics. The results validate that immersion in simulated contexts and professional roles catalyzes the transition from mechanical calculation to engineering judgment, overcoming the traditional model's limitations regarding decision-making under uncertainty. Significantly, the findings challenge the notion of academic resistance; students demonstrated a willingness to assume a higher cognitive and temporal load when they perceive value in their professional training. The study concludes that the CGS model is superior in activating intrinsic ethical responsibility and cognitive confidence, although its large-scale sustainability requires curricular reengineering to address structural limitations regarding time and technical resources.

The main objective of this document is to assist future researchers in identifying fit problems in the development of predictive models used in Machine Learning. To this end, theoretical aspects were addressed, and most importantly, a practical case study was developed using synthetic data. Three predictive models were created from this data: an underfitted model, an overfitted model, and an ideal model. This allowed for the identification of the characteristics of each type of fit. Finally, a simple and practical guide was created outlining the steps to follow to obtain a model with an adequate fit, that is, one with good generalization capabilities.

The diverse teaching strategies and activities implemented in the classroom stem from a process of planning and projection carried out by the teacher. Understanding how this process influences learning and the development of competences is essential for improving educational practice. Therefore, the main objective of this research was to examine the relationship between teaching planning and the development of cognitive skills in university students. The study followed a quantitative approach with a correlational scope and a non-experimental design. The final sample consisted of 175 students enrolled in engineering programs in Lima, Peru, aged between 18 and 42 years (M = 24.87, SD = 5.06). The results indicated a significant, strong positive correlation between teachers’ planning and the development of students’ cognitive skills (r = .642, p < .001, Z = 0.683). Additionally, women scored higher than men in their perception of teaching planning and in critical thinking skills. The group aged 31–42 years showed higher scores in both variables and their dimensions; students who combine work and studies scored higher in cognitive, analytical, and creative skills. No significant differences were found based on the students’ field of engineering. Finally, students in the virtual learning modality scored higher across all variables compared to those in face-to-face and blended learning modalities.

Computational thinking represents a key competency in initial university education and is comprised of fundamental pillars such as algorithmic thinking, decomposition, pattern recognition, and abstraction. This research aimed to determine the relationship between the pillars of computational thinking and the academic performance of first-year university students, as well as the relationship between the pillars of decomposition, pattern recognition, and abstraction with algorithmic thinking. The study adopted a quantitative approach with a non-experimental, correlational design. The population consisted of 40 students enrolled in the Basic Computer Science course. The students' computational thinking was assessed using the Román-González Computational Thinking Test. Academic performance was measured by the final grade obtained in the course unit. Because one of the variables did not meet the assumption of normality, Spearman's rank correlation coefficient was used for data analysis. The results showed no statistically significant relationship between the pillars of computational thinking and academic performance. However, a positive and statistically significant relationship was identified between the pillars of decomposition, pattern recognition, and abstraction with algorithmic thinking. The research results suggest that, while the pillars of computational thinking are not directly associated with academic performance, they do maintain an internal structural relationship that supports the development of algorithmic thinking in first-year university students.

The main purpose of this research was to determine the relationship between spatial intelligence and the design ideation process of architecture thesis students in Trujillo, Peru. This was an applied, correlational, quantitative, and non-experimental study. The sample consisted of 60 architecture thesis students at a private university in Trujillo, Peru. Two questionnaires were administered. The main finding was a strong relationship between the design ideation process and spatial intelligence (p = 0.000 < 0.050), with a high positive correlation (Spearman's rho coefficient of 0.717). This indicates that higher levels of spatial intelligence were associated with improved design adaptation among the architecture thesis students in Trujillo, Peru.

Abstract– Technological advancements have become essential allies across various disciplines and fields of study. In this context, education focused on developing STEM competencies aims to foster the comprehensive growth of university students by equipping them with skills that significantly impact both society and the economy. Consequently, a Literature Review Article (LRA) was conducted to explore STEM competencies in relation to university students' academic and professional development, framed within the Sustainable Development Goals (SDGs). To ensure a rigorous approach, the study followed the PICOC model (Problem, Intervention, Comparison, Outcome, and Context), leading to the construction of a robust search equation for analysis. The initial search yielded 161 articles, which were subsequently filtered using the PRISMA methodology, resulting in 33 research studies that specifically addressed STEM-driven competencies linked to the SDGs. These studies examined key challenges, the primary competencies fostered through STEM education, and the fields of study where these competencies have been implemented. The findings highlight the relevance of developing STEM competencies at the university level, as they equip students with the necessary skills to integrate into the globalized economy. Moreover, STEM education empowers students to propose innovative and sustainable solutions, reinforcing their role as key contributors to societal and economic progress.

Keywords-- Education, competences, STEM, SDGs, university.

This paper reports a preliminary study of students’ perceptions of their ability to understand, analyze, and write software requirements. It describes students’ self-reported confidence and the challenges they face when producing requirements. The goal is to identify teaching approaches that help students write clear and effective requirements that support the full software development life cycle (SDLC). Quantitative results are presented as frequencies and percentages, and qualitative responses are analyzed thematically. The findings show moderate student confidence and common difficulties with ambiguity and with requirements that are not specific enough.