An educational methodology was developed and implemented to increase both argumentative skills and interest as part of students' critical thinking competence, which would allow them to build structure and justified explanations about how engineering systems works. This methodology increases both students' argumentative skills and students' interest in building structured and justified explanations about how engineering systems work. The developed methodology has 3 steps. First, pre intervention evaluation of both argumentative skills and students' interest in developing supported explanations about how engineering systems work. Second, practice of the Affirmation-Justification-Application of sciences laws (AJA) strategy analyzing specific systems and sharing their explanations in a TikTok account. Third, post evaluation of both argumentative skills and the student's interest in developing supported explanations about how engineering systems work. Individual interest in developing structured explanations of how engineering systems work, was studied as a multidimensional construct divided into 4 dimensions: predisposition, affection, intentionality and appreciation. Our study was carried out in the engineering school of a medium size university with 61 students in 2 different campuses. A comparation between the post-test and pre-test argumentative skills using a 90% confidence hypothesis test for difference of paired means shows a significant improvement in this variable. A similar comparison for student's interest in developing structured arguments shows that after using the proposed methodology, students increased their affection, intentionality, and appreciation dimensions, which imply a significant change in their attitude towards engineering systems analysis. This study provides empirical evidence of how a structured methodology for arguing and describing engineering systems can improve competence development of students. We believe that the proposed methodology can be adopted by teachers in other courses to provide students with a framework for their explanations and analysis.

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 41 Engineering students, who practice and apply the Affirmation - Justification - Application of Laws and Principles strategy (AJA) to build an argument about and specific Engineering system. Students’ argumentative skills were measured with a validated questionnaire before (pre-evaluation) and after (post-evaluation) students knew and practiced AJA strategy. Results show a significant improvement in two of the three components of the argumentative students’ skills: Justification and Application of Laws and Principles, but in the Affirmation component, results did not show significant improvement. Evaluations were written as questions where students were asked to explain the behavior of an engineering system in two different contexts: physics context and math context. Physics context questions demanded from students, besides a correct application of AJA strategy, a correct application of physics Newton's laws to explain the movement of a system. Math context questions require from students a good knowledge of graphical interpretation of derivatives in the context of a motion to interpret certain conditions of movement in a specific system. Results show no difference between evaluation in physics and math contexts.

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.

As human beings are complex entities that perceive and assimilate the environment, it is engaging to inquire what are the factors involved in the self-perception of achievement that will allow individuals to be successful. Determining the predictive factors of subjective perception of well-being allows us to know what is affecting student academic performance. Primary relationships are an important element of these factors and have direct impact on the perception of achievement that students may have and the goals they set. This perception allows them to be more resilient in crisis situations and in persevering throughout their studies. This study assessed the subjective well-being perception in 297 students of second year of engineering. A test was used to gather information to determine Subjective Well-being Index. There was found a relationship between Subjective Well-Being and academic performance, it was observed that those stuudents with a higher score are more likely to have a better performance. It is an important fact as recent studies show that a high index works as a mediator to stay motivated and engaged with student career.

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.