In the article, the authors evaluate the computational thinking skills according to gender of a group of male and female students of industrial engineering and systems engineering from universities located in the Andean region of Peru; the five key skills were evaluated: abstraction, decomposition, generalization, algorithmic design, and evaluation. To strengthen computational thinking, activities related to agriculture, livestock, the environment, safety, and education were proposed, which are of interest to the community where the students live. The research methodology followed is quasi-experimental of the post-test type with intentional non-probabilistic sampling. During the development of the activities, the students used microcontrollers, sensors, and actuators; thus, they also used block-based programming to implement hardware and software prototypes. The results have shown, according to the inferential analysis, that there are no significant differences between male and female students in any of the computational thinking skills. These results were due to the educational strategy applied in the development of STEM activities, which focused on solving real problems in the student community and generated the same enthusiasm in female and male students compared to other activities that only generated motivation in male students.

In this work, the authors propose the use of technological resources to develop computational thinking following the steps or phases of problem-solving for first-year students. During the development of the activities using technological resources (Arduino board, sensors, electronic devices, and mBlock) the students carried out activities, such as algorithm development and programming of the Arduino board and sensors from a friendly and playful interface such as the mBlock, as well as the debugging of programs until obtaining the expected results. These activities had an impact on the cognitive processes, practices, and technological perspectives of the students. Causality has been shown to exist between computational thinking skills and problem-solving phases in an environment of engineering students entering college. For the analysis of the relationship between computational thinking skills and problem-solving, Pearson’s statistical correlation test was used through SPSS software.

Background/Objectives: This study presents a Convolutional Neural Network (CNN) approach for detecting depression and schizophrenia using motor activity patterns represented as images. Participants’ motor activity data were captured and transformed into visual representations, enabling advanced computer vision techniques for the classification of these mental disorders. The model’s performance was evaluated using a three-fold cross-validation, achieving an average accuracy of 95%, demonstrating the effectiveness of the proposed approach in accurately identifying mental health conditions. The objective of the study is to develop a model capable of identifying different mental disorders by processing motor data using CNN in order to provide a support tool to areas specialized in the diagnosis and efficient treatment of these psychological conditions. Methods: The methodology involved segmenting and transforming motor activity data into images, followed by a CNN training and testing phase on these visual representations. This innovative approach enables the identification of subtle motor behavior patterns, potentially indicative of specific mental states, without invasive interventions or self-reporting. Results: The results suggest that CNNs can capture discriminative features in motor activity to differentiate between individuals with depression, schizophrenia, and those without mental health diagnoses. Conclusions: These findings underscore the potential of computer vision and deep neural network techniques to contribute to early, non-invasive mental health disorder diagnosis, with significant implications for developing mental health support tools.

This study analyzed the implementation of a problem-solving method based on Pólya’s proposal, complemented by accessible technological resources such as the Arduino board, sensors, and STEM educational cards, in engineering and nursing students from public universities in Peru. A quasi-experimental design with pre- and post-test was used, employing a quantitative approach and intentional non-probabilistic sampling. The participants were 98 first-year students who developed formative research projects contextualized to their local reality, using the visual programming environment mBlock. The results show significant development of research competencies in both majors, especially in the solution review phase, evidencing critical thinking and reflective evaluation. No significant differences were found between the majors regarding the use of educational technology, reinforcing its cross-disciplinary applicability. It is concluded that the combination of Pólya’s method and the use of accessible technologies strengthens active, reflective, and contextualized learning in higher education.

This study aimed to analyze the observed variations in the research competencies of first-year nursing students following the implementation of a pedagogical problem-solving strategy based on Polya’s method and integrated with STEM technological resources. The study was conducted within the context of higher education, where strengthening research skills in the early stages of academic training continues to represent a formative challenge. A pre-experimental pretest–posttest design with a single group was employed, involving 69 students enrolled in an introductory research course. The intervention was implemented over 16 weeks and structured according to the four phases of Polya’s method—understanding the problem, planning, execution, and review—while also integrating the use of a STEM educational kit and visual programming tools. The results showed descriptive increases across all evaluated dimensions. The overall mean score increased between the pretest and posttest, revealing statistically significant differences in research competencies (p < 0.001) and a large effect size (r = 0.896). Likewise, significant differences were identified in the dimensions related to research background and objectives, development of research activities, and evaluation of results. In contrast, the dimension concerning problem identification and formulation did not show statistically significant differences, suggesting that this competency may require longer formative processes and greater academic support. Although the results suggest favorable associations between the implemented strategy and the observed variations in research competencies, these findings should be interpreted with caution due to the limitations inherent to the pre-experimental design employed. In this sense, the study provides preliminary evidence regarding the pedagogical potential of integrating problem-solving and STEM technological resources to promote the early development of research competencies in university students in the health sciences.

The Problem- and Technology-Based Research (IBPT) approach constitutes an innovative pedagogical model designed for first-year university students, with the objective of strengthening research and technological competencies through the integration of technological resources, problem-solving, and formative research. The aim of this research was to introduce and validate IBPT as a viable option for higher education by evaluating its effects on the gradual enhancement of research skills among 73 first-year engineering and nursing students at a public university in Peru. A quasi-experimental pretest-posttest methodology was utilized, incorporating a questionnaire designed according to the four phases of IBPT: understanding the problem, planning activities, executing tasks, and reviewing solutions. The findings revealed significant improvements in research competencies, particularly in activity execution and solution review, while more moderate progress was observed in problem comprehension and activity planning. In conclusion, IBPT emerges as an effective alternative for integrating problem-solving and technological resources in higher education, fostering the acquisition of research competencies and laying the groundwork for the consolidation of advanced research skills, essential for academic knowledge production and professional development in later stages.