This study presents an advanced educational intervention conducted in the Fundamental Mathematical Modeling (MA1028) course for first-semester engineering students. By integrating sophisticated mathematical modeling techniques with prototype design and construction, students actively engaged in applied research processes. This approach not only strengthened technical competencies but also fostered interdisciplinary collaboration, developed transversal skills, and embedded research principles into foundational engineering education. In this project, students applied advanced mathematical frameworks using MATLAB to model physical systems and SolidWorks for structural design. Prototypes were constructed with cutting-edge 3D printing and laser-cutting technologies, enabling students to transform theoretical models into practical solutions, thus reinforcing both analytical and hands-on skills. Exposure to civil engineering literature, particularly in bridge construction, broadened their understanding of real-world applications of mathematical principles. A distinctive aspect of this initiative was fostering an environment centered on applied research. Interaction with civil engineering experts provided students with technical guidance and practical insights into structural design, bridging academic theory with industry practices. The intervention divided the class into a control group, focused solely on theoretical problemsolving, and an experimental group that also engaged in physical model construction. Evaluations revealed that the experimental group achieved superior mastery in specific and transversal competencies, including problem-solving and interdisciplinary collaboration. This educational model underscores the value of early research engagement, equipping students with a researchoriented perspective crucial for tackling the complexities of an evolving engineering landscape. The results confirm the profound impact of applied research in developing engineering competencies and preparing future professionals.

Statistical Analysis is a core second-semester course for engineering students, designed to provide a foundational understanding of regression analysis and introductory concepts in time series. The course spans five weeks, with a total of 20 instructional hours, and is offered in the flexible, digital ELITE modality, which leverages synchronous web conferencing via Zoom and extensive interaction through the Canvas Learning Management System (LMS). This innovative modality accommodates over 100 students distributed across multiple university campuses, fostering a collaborative and immersive learning environment. The course is led by a distinguished faculty member from the institution, supported by a multidisciplinary team of tutor professors stationed at different campuses. These tutor professors play a pivotal role in offering tailored feedback, academic guidance, and close monitoring of student progress. Additionally, guest experts from industry are integrated into two to three sessions, providing real-world insights into the professional applications of linear regression and time series analysis, thereby bridging academic concepts with industry practices. Active learning strategies are embedded within the course structure. All instructional materials are systematically organized on the Canvas LMS platform. The course culminates in a cross-campus collaborative project, which is designed to apply linear regression and time series concepts to analyze global indicators of happiness. To ensure robust evaluation, the course integrates two asynchronous argumentative exams which serve as key learning evidence, assessing the development of competencies in statistical reasoning and problem-solving. Through its innovative digital structure, cross-campus collaboration, and industry integration, this course exemplifies a cutting-edge approach to engineering education in the digital era.

We present the design and validation of an anthropomorphic prosthetic hand with $\mathbf{1 8}$ degrees of freedom (15 flexion-extension and 3 MCP ab/adduction) actuated with a single actuator. A cable-driven differential with nested pulleys redistributes tension so that the four fingers progress sequentially and adaptively upon contact, enabling conformal grasps with a single command. The fingertip trajectory is modeled as a logarithmic (equiangular) spiral consistent with neurophysiology, and the fingers’ elastic return constants are identified so as to reproduce human-like kinematics. We analyze the force flow of the differential for cylindrical and non-cylindrical objects and demonstrate adaptive grasping of everyday items. A standardized evaluation protocol is described to align experiments with the kinematic and force analyses, supporting reproducibility.

In this article, we present the innovative results achieved by successfully integrating smartphone sensors and mobile technology into online teaching of differential equations, with a focus on engineering students. The outcomes of this methodology reveal significant benefits, including increased motivation and active participation among students in the experimental group compared to the control group. The proposal of this innovative approach is based on an effective combination of experiential learning and the practical application of tools such as Physics Toolbox Sensor Suite and MATLAB. This approach enriches students' learning experience and translates into notable improvements in their academic performance. Students demonstrate heightened engagement and motivation by being able to directly relate the theory of differential equations to practical applications and real-life situations. The methodology used represents an evolution in online mathematical education, making differential equations accessible and applicable to all students, paving the way for a more effective and motivating online education.

In this article, we present the results of a study carried out in the subject “Chemical Experimentation and Fundamental Statistical Thinking in Engineering” (Q1021) with engineering students from Tecnológico de Monterrey. In this study, we applied an innovative learning approach that combines gamification and experiential learning. Our project focused on improving the understanding of oxidation-reduction reactions in the field of chemistry. Instead of relying on traditional memorization methods, we implemented a pioneering approach that involves gamification and hands-on experience. The students actively constructed electrochemical cells and miniature vehicles powered by these cells. Subsequently, they participated in various speed and distance competitions. This approach not only helped retain key concepts but also encouraged a deep understanding of the underlying processes, requiring the practical application of physics and mathematical principles. The results demonstrated a notable improvement in understanding oxidation-reduction reactions and specific skills, surpassing the control group. These results are not limited only to the field of chemistry but also suggest the possibility of applying these innovative approaches in the teaching of chemistry in other related disciplines as physics, mathematics and electricity. This interdisciplinary approach proves to be a highly effective strategy to promote deep and long lasting learning in various areas, with potential significant implications for pedagogy in higher education and the teaching of Exact Sciences.

In this article, we present the results of an experiment conducted in collaboration between the School of Engineering and Science (EIC) and the School of Business (EN) the Tecnologico de Monterrey, involving second-semester students and the company Labopat as a training partner. The experiment took place within two multidisciplinary courses, F1015B “Application of Thermodynamics in Engineering Systems” and EM1001B “Ideation and Prototyping.” The research design employed a quasi-experimental approach, comparing two distinct teaching methods. One method employed a multidisciplinary approach in collaboration with the training partner, while the other followed a traditional approach in which students tackled a real-world issue faced by the company. This unique educational experience provided students with a valuable opportunity to apply their theoretical knowledge to real-world situations, fostering the development of essential skills and competencies. These included the ability to solve complex problems, think critically, and effectively collaborate within interdisciplinary teams. Significantly, the project not only strengthened students' academic foundations but also made a substantial contribution to the growth and development of the collaborating company. The company benefited from the effective resolution of its real-world challenges. The obtained results underscore the value of collaboration between academia and the business sector, with a focus on innovative and practical solutions. This study highlights the mutual benefits of such collaboration, emphasizing its potential to bridge the gap between theory and practice while preparing students for their future careers.

In this article, we present the impact of implementing MATLAB as a pedagogical tool in teaching the subject “Analysis of the Structure and Transformation of Matter” (Q1029) during the period from October to November 2022 and 2023, spanning five weeks, in both periods. In this educational experience, we provided a MATLAB script to a group of 32 students as an integral part of their final project. Surveys conducted before and after the introduction of MATLAB revealed a significant increase in efficiency, rising from an average of 8.49 to 9.37. This increase not only suggests a deeper understanding of chemical concepts and calculations but also a substantial improvement in students' problem-solving skills. Additionally, we explored students' perceptions through a questionnaire about the use of MATLAB. The results indicate a high degree of impact and motivation, with approx. 73% of participants expressing their approval. Over 50% of students claimed that the introduction of MATLAB allowed them to discover new concepts, highlighting its positive impact on the learning process. The active integration of MATLAB into the teaching of the subject “Analysis of the Structure and Transformation of Matter” (Q1029), undertaken by us, not only optimizes students' efficiency and understanding but also promotes the development of practical programming skills applied to chemistry, preparing them to face future technological and scientific challenges. This innovative approach aligns with contemporary educational demands and provides an enriching perspective for university learning.

The use of artificial intelligence and machine learning allows us to define better strategies to investigate the general performance of each student and establish predictive strategies, which is why it represents an excellent early warning system, allowing the identification of students at risk of failing subjects in advance and help them. In this study, we used artificial intelligence and the techniques K-Nearest Neighbors, Support Vector Machine (SVM), Naive Bayes, Random Forest Classification to study a sample of 715 high school students from Tecnologico de Monterrey, in Hermosillo and predict the risk of failure per student. Our model showed excellent performance in predicting failed subjects. Our model demonstrates that machine learning is a robust tool for identifying students at risk of failing subjects early and helping them.

In this article, we present the results of a study carried out in Engineering modeling through a dynamic systems class with engineering students from Tecnologico de Monterrey, México, where we applied challenge-based learning for 10 weeks. The implemented methodological process consisted of carrying out 4 laboratory practices and comparing the experimental results obtained regarding the results obtained in terms of the algebraic solution and by numerical methods, as relevant information of this method was the final elaboration of an argumentative text that allowed to arrive at the most objective truth possible. The research design was quasi-experimental, where two different teaching methods were compared using control and experimental groups applied in other groups. The results obtained allowed us to validate that the use of challenge-based learning in engineering modeling classes using dynamic systems (mathematics) made it possible to eliminate the difficulties presented by students both to understand the need for demonstrations in mathematics and to perform them. Challenge-based learning made it possible to avoid the trend of algorithmic mathematics in the classroom, avoiding the mechanical knowledge of formulas and their routine application, allowing students to solve problems of different levels of complexity, evaluate various technologies, and, above all, with openness in the search and implementation of relevant alternatives in the transformation of professional practice.

The coronavirus pandemic has changed the way education is delivered, and because of this problem, challenges and new needs have arisen that have led us to adapt the way we teach and evaluate. In this context, much work has been done to bring students learning experiences in new modalities to safeguard the well-being of all its participants. Virtual reality in engineering education plays an important role because students can acquire the concepts, knowledge, skills, and competencies more completely through immersive, multi-sensory, and credible environments arising as a flexible resource to improve engineering education in a post covid era.The use of virtual reality in the teaching-learning process is an innovation in teaching didactics and constitutes a very important opportunity to guarantee quality in the training of future engineering students. In parallel, the technological advances brought about by the industrial revolution that is taking place have increased the levels of complexity in terms of cost, product design, innovation, implementation, performance evaluation, to name a few.

In this paper, we present the results of a study carried out with Mechatronics engineering students at the Tecnologico de Mexico in Hermosillo, Mexico. The purpose of this research was to design a profile that will be the goal for our students inside of Internet of Things (IoT) laboratory.Students do not have training that allows them to acquire intellectual property. For this, it seeks to train them in an emerging technologies laboratory where students can develop projects from which they can acquire the exclusive right over the technology and generate in them the necessary skills to achieve it.

Currently, the physics subjects taught professionally at Tecnologico de Monterrey, Campus Sonora Norte, lack adequate equipment, instruments, and supplies for laboratory practices; therefore, learning provides the necessary basis for the level of education to which our institution aspires for its students. Our project aimed to improve student performance through the use of programs such as AP-Physics and PhET, Wolfram demonstrations, construction of laboratory equipment, preliminary preparation of a laboratory manual, use of laser cutter and 3D printing, peer evaluation, and self-assessment.The design and construction of prototypes enabled measurements, analysis and engineering design, and the ordered and logical process necessary to produce valid results. In addition, it resulted in the strengthening of theory and the development of social values and communication.