Conference Agenda

Abstract—The integration of peer mentoring into engineering academic programs as a mechanism to support students facing academic difficulties has received considerable attention in higher education worldwide, particularly in developed countries. This study employed a mixed-methods review approach to examine the benefits of peer mentoring through a program implemented by high-achieving engineering students. The study's findings underscore the significance of implementing peer mentoring as a support framework for students grappling with critical challenges, owing to its manifold advantages. Furthermore, it emphasizes the necessity of ascertaining the diverse factors that contribute to the efficacy of peer mentoring programs.

The Peer Mentoring of Excellence (PME) Program has demonstrated a consistent track record of maintaining satisfaction rates in excess of 90%. The study's findings indicate that collaborative learning with peers fosters an environment conducive to learning, thereby enhancing students' knowledge and skills. In a similar vein, students who act as mentors have attested to the development of leadership, communication, and assertiveness skills that has resulted from their engagement in the program. Furthermore, these students have noted that their participation serves to reinforce the knowledge they have acquired.

This study proposes a pedagogical model aimed at strengthening the relationship between academic training and employability in technical-professional education. Based on a university case study conducted between 2020 and 2025, the research analyzes the association between training processes and graduate labor market insertion through a quantitative, non-experimental design. Descriptive and inferential statistical analyses, including chi-square tests, ANOVA, and logistic regression, were applied to identify differences across academic programs and to determine the predictive effect of composite employability scores on employment status. The findings reveal that employability outcomes are not solely determined by technical competencies, but rather by the degree of pedagogical articulation between curriculum design, industry linkage, competency certification, and graduate follow-up mechanisms. Significant variations across programs confirm that institutional coherence and structured collaboration with the productive sector play a decisive role in employment outcomes. In response to these findings, the MEFEP Pedagogical Model is proposed as an integrative framework that aligns competency-based training, experiential learning, industry engagement, and continuous evaluation mechanisms to enhance graduate employability and institutional relevance.

The study analyzed the use of the Model Canvas adapted to scientific research as a pedagogical strategy in higher education. A quantitative approach with a quasi-experimental design was employed, comparing two intact groups of students from administration and marketing programs (N = 40). The experimental group integrated the Model Canvas into the development of the academic research product, while the comparison group followed the traditional methodology. Outcomes were assessed using rubrics that considered product quality, coherence and planning of the research process, and student autonomy and self-regulation. The analysis included descriptive statistics, the Mann–Whitney U test, effect size estimation (Glass’s delta), and a complementary Bayesian analysis. The findings revealed significant differences in favor of the experimental group in product quality and coherence and planning, while no conclusive differences were observed in autonomy. It is concluded that the Model Canvas constitutes an effective pedagogical strategy for improving the structure and quality of research products in higher education.

Designing effective vaccination strategies remains a significant challenge for urban health systems, particularly in settings characterized by high population density, operational limitations, and pervasive uncertainty. This study addresses this challenge through a simulation-based and mathematical modeling framework aimed at evaluating and comparing alternative vaccination strategies at the district level.

The proposed approach begins with an optimization formulation based on systems of differential equations derived from a stratified compartmental model, which is subsequently transformed through linearization into a linear programming optimization framework. The model integrates demographic characteristics, vaccination coverage levels, and operational assumptions to assess the impact of different scenarios on overall system performance.

The methodology is applied to a district within the metropolitan area of Lima, Peru—currently home to more than 11 million inhabitants—enabling the mathematical simulation and statistical evaluation of multiple vaccination strategies under varying degrees of service coverage complexity. The results demonstrate that the proposed framework generates actionable quantitative insights to support public health decision-making, facilitating the identification of strategies that enhance coverage efficiency and improve system-wide performance. Overall, the study underscores the value of analytical and modeling tools as effective decision-support mechanisms for vaccination planning in urban contexts across Latin America.