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.

Development of argumentative skills is fundamental for Engineering students, allowing them to identify assumptions and build conclusions about observed phenomena. In this paper we report results from a study involving 65 Engineering students, who use TikTok as a learning platform applying the Affirmation - Justification - Application (AJA) of the laws of Physics strategy to build an argument about and specific Engineering system and were asked to answer pre and post questionnaires about the use of social media for improving argumentative skills. Results show dependency between technological aversion and students' perception of skill development, where students who consider that TikTok can enrich their learning process show a significant increase in their perception of argumentative skills development; however, those students who have a prior discomfort with the social platform, present a negative perception of skill's development.

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.

During the learning process of multivariable calculus, the spatial perception of 3D surfaces represents a major challenge for the students. Since it requires the ability to sketch the graph in a 3D system, a handmade sketch can be confusing and lead to misinterpretations of other concepts. In the specific case of triple integration, the task of setting the limits of a solid requires the combination of a good visuospatial competency plus a correct management of symbolic language of functions. Nowadays, one of the most relevant objectives for using mathematical visualization programs and/or technologies is to improve the spatial perception of 3D surfaces. In this work, a comparison between the use of tailor-made Augmented Reality software and Matlab® for visuospatial skills development in multivariable calculus course, is presented.

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.