The profile of undergraduate students that partici-pate in a face-to-face entrepreneurship multidisciplinary course is presented. The course is part of the engineering curricula in an university of Mexico. Entrepreneurial experiences, background, interests, attitudes, intention and perception of entrepreneurial skills of the participant students are addressed through this paper. The experimental protocol consisted of a pre-test, intensive entrepreneurship activities, with experiential and sustainable perspective, and a post-test. Data were collected by applying a validated questionnaire to fifty two students. The questionnaire consists of eighty questions on a four-point Likert scale. After a rigorous statistical analysis, results show that this course positively impacted the perceived organizational entrepreneurial culture and skills, attitudes, and interests in the participant students, and offers valuable data for decision making to impact the entrepreneurial intention to start a business in the short-term.

The main goal of this research is to propose a quantitative approach to identify critical thinking in undergraduate engineering students by leveraging electroencephalogram (EEG) measurements, facial emotion recognition and machine learning techniques. Measurements were taken by recording the face of the subjects and acquiring EEG signals from a wearable device. We explore the use of chaotic descriptors such as Lyapunov exponents, fractal dimension, Hurst exponent and approximate entropy to extract meaningful information from EEG signals. Subsequently we tested different machine learning algorithms to classify EEG signals and analyze the onset of critical thinking. An experiment was conducted in which students engaged in problem-based learning scenarios while wearing portable EEG devices. The collected data were then analyzed to discern patterns and discriminate between relevant, irrelevant, and false information in EEG signals. The experimental setup involved measuring the EEG signals from engineering students as they solved problem-based learning scenarios. The results demonstrated the ability to distinguish between different sections of a video based on brain activity, with false, relevant, and irrelevant information. Some findings include the left frontal electrode with the theta frequency band exhibiting the highest distinguishability for relevant information. From the experiments, we identified the Hurst exponent and approximated entropy as the most relevant descriptors for classification. The main contribution of this study is to offer an approach to decode critical thinking in engineering undergraduate students using EEG measurements and machine learning techniques.

The main purpose of this research is to decrease the anxiety levels of undergraduate engineering students during a Mathematics exam; those anxiety levels are intended to be decreased through a five-week Mindfulness program. We propose a quantitative measurement of anxiety levels using EEG signals from a portable device, those raw signals are processed using chaotic descriptors. Levels of anxiety are measured before and after a Mindfulness program before solving a couple of online Advanced Mathematics (complex variable) exams. Critical thinking skills are also measured from EEG before and after the Mindfulness program during a Problem-Based Learning assessment. Results show a decrease in anxiety levels and an increment in critical thinking skills.

This paper present different low-cost alternatives for virtual laboratories to use during home schooling for mechatronics engineering courses. Due to the limitations with home offices and home schooling, it was necessary to look for alternatives to be able to carry out simulations in the engineering area that would help to solve the deficiencies in terms of laboratories on campus. In this work we focus on searching and developing alternatives for solutions in control engineering, programming, and automation with PLC. This work present five low-cost alternatives are presented so that students can live an enriching experience with the development of skills. A comparison is made between these alternatives.

Tecnologico de Monterrey has implemented the Tec21 model, an innovative education model consisting of four components: challenge-based learning, flexibility, inspiring teachers and memorable experience. Challenge-based learning technique faces the student to real problems, binding him with his environment by challenging experiences, in this context this technique was applied to three different groups with students of different engineering programs through a challenging activity in order to evaluate competencies defined for the courses of Control Engineering and Control Systems. In this paper, results of this evaluation are presented, and a comparison is made between traditional and Semestre i courses.

This paper shows the development, implementation and results of an i-Semester, as part of the new educational model launch by Tecnologico de Monterrey, linked to the current needs of the automotive industry, whose companies are located throughout the Mexican Republic. The i-Semester program is described as an undergraduate training program, aimed to the development of disciplinary and transversal outcomes, defined by the Accreditation Board for Engineering and Technology, through industrial experiences and real-life applications. It is explained how the i-Semester was applied to an automotive industry challenge, where the student was involved in design, analysis and validation of components for the automotive industry. Explaining the work scheme, the modules with learning content, as well as the development of the industrial challenge. This work explains the experience in two editions of i-Semester in conjunction with a training partner, the automaker company Volkswagen de México. Thirty-eight students of Automotive Design Engineering and Mechatronic Engineering careers were immersed for 18-week periods, during spring and fall 2017 each one, into instruction from the teachers, professional workshops within the company and real-life experience to solve on real-life industry problem. Six teachers from the university and five engineers from the company served as mentors, giving feedback to students looking to solve the industrial problem. The second part of the paper explains how the evaluation tools were developed and implemented. As a conclusion, comments are made on the implementation challenges, opportunities, advantages and disadvantages that this new model has with respect to the traditional education scheme.