People are increasingly open to using online education mainly to break the distance and time barriers of presential education. This type of education is sustainable at all levels, and its relevance has increased even more during the pandemic. Consequently, educational institutions are saving large volumes of data containing relevant information about their operations, but they do not know why students succeed or fail. The Knowledge Discovery in Databases (KDD) process could support this challenge by extracting innovative models to identify the main patterns and factors that could affect the success of their students in online education programs. This work uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to analyze data from the Distance Education Center of the Universidad Católica del Norte (DEC-UCN) from 2000 to 2018. CRISP-DM was chosen because it represents a proven process that integrates multiple methodologies to provide an effective meta-process for data knowledge projects. DEC-UCN is one of the first centers to implement online learning in Chile, and this study analyses 18,610 records in this period. The study applies data mining, the most critical KDD phase, to find hidden data patterns to identify the variables associated with students’ success in online learning (e-learning) programs. This study found that the main variables explaining student success in e-learning programs are age, gender, degree study, educational level, and locality.

Este estudio presenta la experiencia del uso de Arduino y la plataforma Tinkercad para el desarrollo de competencias de programación en estudiantes de primer año en la Universidad Continental de Huancayo (Perú). El estudio es desarrollado en línea durante tiempos de la pandemia COVID-19 con uso de un simulador. El desarrollo de competencias de programación es un hito fundamental y esencial para estudiantes de carreras de ingeniería de sistemas, ciencias de la información, computación e informática. Para los estudiantes, resulta complejo trabajar con lenguajes de programación que requieren el cumplimiento de reglas tanto sintácticas y semánticas complejas. Los resultados demuestran un impacto positivo en el desarrollo de competencias de los estudiantes respecto a cursos previos, además de desarrollar competencias básicas del área de circuitos digitales y electrónica computacional. Se concluye que los estudiantes se sienten muy cómodos con el uso de Arduino y Tinkercad, aun cuando por la pandemia, los experimentos no fueron desarrollados de forma física.

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from $\mu =3.9$ (Challenge phase) to $\mu =4.6$ (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education.

The rapid integration of Industry 4.0 technologies into non-computer engineering curricula presents a significant pedagogical challenge: avoiding a “one-size-fits-all” approach. While Project-Based Learning (PBL) is widely advocated for teaching Internet of Things (IoT), little research addresses how students from different engineering branches—specifically Industrial, Environmental, and Electrical—respond to identical technical requirements. This study evaluates the deployment of ESP32-based IoT solutions for local agriculture and beekeeping problems in the Peruvian Andes, analyzing the performance and perception of three distinct student cohorts (Total N = 95). Results indicate a significant divergence in learning outcomes and satisfaction. The cohort predominantly composed of Industrial Engineering students (NRC-33563) demonstrated lower adherence to technical code modularization (88% vs. 97%) and lower overall course recommendation rates compared to the mixed cohorts (NRC-33562/33561), who reported higher engagement with the hardware implementation. These findings suggest that while Environmental and Electrical engineering students naturally align with the sensing and actuation layers of IoT, Industrial engineering students may require a curriculum that emphasizes process optimization and data analytics over raw firmware development. We propose a differentiated pedagogical framework to maximize engagement and competency acquisition across diverse engineering disciplines.

A persistent interdisciplinary gap continues to hinder the development of Health 4.0 educational initiatives. Biomedical Engineering programs typically emphasize physiology and instrumentation while providing limited exposure to modern software ecosystems, whereas Informatics curricula often overlook the physical and physiological foundations of bio-instrumentation. To address this dual deficiency, this paper presents a low-cost and modular educational intervention aligned with the “Computing, Electronics, and Health for Everybody” philosophy. The proposed approach is a hands-on technical workshop that translates core biomedical signal-processing concepts into an accessible learning experience using the Arduino platform and the AD8232 ECG sensor. The intervention was implemented simultaneously across universities in Chile, Peru, and Ecuador, involving a total of $n=92$ undergraduate engineering students. Learning outcomes were evaluated using a pre–post assessment design. The results demonstrate a statistically significant improvement in participants’ conceptual understanding of ECG signal components ($p<0.001$), with mean scores increasing across all evaluated dimensions. In addition, students reported higher confidence in interpreting physiological signals and applying interdisciplinary reasoning. These findings indicate that the proposed intervention effectively supports interdisciplinary learning for software-oriented engineering students by introducing core biomedical acquisition and signal-processing concepts through an accessible and scalable educational framework.

Model-Based Diagnosis (MBD) is widely used to identify minimal conflicts and repair actions in constraint-based systems. Recent advances in parallel reasoning have significantly reduced runtime in large-scale models through speculative and multicore execution strategies. However, existing approaches primarily focus on computational efficiency and implicitly assume that minimal diagnoses are inherently suitable explanations for human decision makers. In complex configuration environments, minimality does not necessarily imply interpretability, as diagnoses may involve structurally dispersed or semantically heterogeneous constraints. To address this limitation, this paper introduces a multi-objective explainability-aware framework for parallel MDB. Diagnosis selection is formulated as a Pareto optimization problem balancing total computational cost and a formally defined interpretability penalty. Interpretability is quantified using graph-based structural dispersion, semantic entropy, hierarchical complexity, and ambiguity metrics. The proposed E-ParetoDiag algorithm computes non-dominated diagnoses and identifies balanced knee-point solutions without modifying correctness guarantees of underlying diagnosis algorithms. Experimental evaluation on large-scale benchmark datasets demonstrates a measurable trade-off between runtime and interpretability, particularly in dense constraint systems. Comparative analysis against classical selection strategies shows that the proposed approach reduces structural dispersion by up to 18% while increasing computational cost by only 7%. Statistical validation confirms that these improvements are significant (p < 0.01) in medium- and high-density scenarios. The results indicate that aggressive parallelism may improve computational efficiency while increasing explanation complexity, highlighting the need for multi-objective selection strategies. Overall, the proposed framework extends scalable symbolic reasoning toward a human-centered diagnosis paradigm and establishes a principled foundation for explainability-aware optimization in constraint-based systems.