Integration of Technological Resources and Problem-Solving Method for the Development of Research Competencies in Engineering and Nursing Students from Two Public Universities in Peru

Abstract

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.

1. Introduction

In the current context, the training of professionals capable of addressing complex and dynamic problems requires the implementation of active methodologies, approaches, and educational and technological strategies that strengthen critical thinking, autonomy, and creativity (Espinoza, 2020; Lozano-Ramírez, 2020). Formative research stands out as a pedagogical process aimed at fostering students’ ability to investigate, reflect, and build knowledge from their earliest academic experiences (Alvarado et al., 2020; Lapa-Asto et al., 2019).

The development of research competencies from the early years of university education is essential for students to acquire key skills such as problem formulation, hypothesis development, project design and execution, results analysis, and effective communication of conclusions (Restrepo Gómez, 2017). Early incorporation of research activities strengthens critical thinking, analytical capacity, and autonomy, while fostering intrinsic motivation toward learning (Cabrera-Berrezueta et al., 2020; López et al., 2018). Likewise, it promotes a positive attitude toward science and technology, enhancing students’ willingness to address and solve problems in their context (Paucar-Curasma et al., 2024b; Yasar, 2018), thus laying the foundation for the training of innovative, socially responsible professionals committed to sustainable development.

Formative research is understood as a pedagogical approach that promotes, from the early university cycles, systematic inquiry, critical reflection, and active knowledge construction (Espinoza, 2020; Salguero et al., 2025). Its purpose is to connect theory with practice through learning experiences that encourage the formulation of questions, the search and analysis of information, and the development of evidence-based solutions to problems within the academic and social context (Lapa-Asto et al., 2019). Several studies have shown that this approach significantly contributes to increasing intrinsic motivation, learning autonomy, and the development of academic reading, writing, and communication skills, while fostering transversal competencies such as collaborative work and argumentative capacity (López et al., 2018; Turpo-Gebera et al., 2020). Moreover, by positioning students as active participants in the research process, formative research strengthens their academic and professional identity, creating conditions for active participation in communities of practice and for the transfer of learning to real-world settings (Diaz et al., 2017; Dipas et al., 2022; Hernández et al., 2020).

Polya’s method (understand the problem, develop activities, execute activities, and review the solution) provides a structured framework for developing higher-order thinking processes, fostering metacognitive reflection, and the ability to transfer learning to new contexts (Olaniyan et al., 2015). Its usefulness has transcended mathematics, demonstrating effectiveness in developing abilities to model, evaluate, and communicate solutions in areas as diverse as engineering, computer science, social sciences, and technological education (Olaniyan & Govender, 2018; Pólya, 1945). This methodological adaptability is reinforced when integrated with active teaching strategies and resources that allow students to directly engage with real or simulated problems (Gardose et al., 2025).

In parallel, the incorporation of accessible educational technologies—such as Arduino, sensors, STEM kits, and block-based programming environments like mBlock—has been shown to enhance active learning, theory–practice transfer, and the development of scientific, digital, and complex problem-solving competencies (Fronza et al., 2019; Marín-Marín et al., 2024; Ortega & Asensio, 2021). These tools not only facilitate conceptual understanding but also foster creativity, autonomous exploration, and collaborative work. Various studies in secondary and higher education report significant improvements in conceptual understanding, solution design, and reflective evaluation when integrating resources such as interactive simulations, graphical organization of ideas, and rapid prototyping processes (Arbeu-Reyes et al., 2024; Chacón-Castro et al., 2023). Thus, the synergy between a clear methodological framework such as Polya’s and the pedagogical use of educational technologies creates a learning environment that fosters critical thinking, innovation, and the training of professionals capable of addressing complex challenges in changing environments.

From the literature review, although the effectiveness of Polya’s problem-solving method and the use of technological resources in diverse educational contexts has been demonstrated (Hamid et al., 2022; Jahudin & Siew, 2024; Klever, 2021), no studies that integrate both elements to strengthen research competencies in engineering and nursing students at public universities in Peru, particularly in the early stages of training, have been identified. This lack of research in an interdisciplinary and local context limits the understanding of its applicability and effectiveness in academic realities with accessible technological resources and contextualized learning needs.

The general objective of this study was to analyze the effectiveness of implementing a problem-solving method based on Polya’s proposal, complemented with accessible technological resources, to strengthen research competencies in engineering and nursing students from two public universities in Peru. Within this framework, three specific objectives were proposed: to assess students’ level of research competencies before and after the educational intervention; to compare the evolution of these competencies between engineering and nursing students; and to identify the phases of Polya’s method in which the greatest progress is evident.

2. Literature Review

2.1. Formative Research

Formative research, understood as part of the university’s pedagogical function, is supported by investigative teaching methods and practices that have proven effective in promoting inquiry, critical reflection, and the active construction of knowledge from the early academic cycles (Lapa-Asto et al., 2019). This approach, developed within a formal curricular framework, is characterized by being led by a teacher as part of their educational role and by involving students who, although not professional researchers, develop skills to formulate problems, propose hypotheses, design and execute projects, analyze results, and communicate conclusions (Llulluy-Nuñez et al., 2021; Zúñiga-Cueva et al., 2021).

Its effective implementation requires coordinated work between teachers and students, as well as appropriate methodological strategies, scientific evaluation, and continuous feedback, in order to strengthen basic research competencies in the initial stages of university education (Alvarado et al., 2020). Furthermore, it fosters positive attitudes toward science and technology, promoting autonomy and intrinsic motivation for learning (Cabrera-Berrezueta et al., 2020). However, it faces limitations arising from the limited time available in the curriculum and from the lack of rigor characteristic of scientific or technological research, especially when technological resources must be adapted to students’ cognitive level (Paucar-Curasma et al., 2022a; Sánchez Carlessi, 2017).

2.2. Pólya’s Problem-Solving Method Through the Use of Technological Resources

Arbeu-Reyes et al. (2024) emphasize that, in today’s digital era, access to information and the ability to conduct research effectively are essential competencies for achieving lifelong success. These skills are even more relevant when addressed systematically and didactically, with a focus on solving social problems and integrating digital tools into the teaching–learning process. This approach not only facilitates and enriches the research process but also comprehensively prepares students to navigate an ever-changing and increasingly digital world. Additionally, it contributes to shaping more competent, critical, and socially committed individuals capable of identifying and solving global challenges.

The problem-solving method proposed by George Pólya has been widely recognized for its pedagogical value in developing higher-order cognitive skills, particularly in disciplines grounded in logic and structured reasoning. Its four-stage approach—understanding the problem, devising a plan, carrying out the plan, and reviewing the solution—constitutes a valuable heuristic framework applicable in both traditional educational settings and technology-mediated environments (Lu & Xie, 2024; Öndeş, 2025).

In recent years, various studies have demonstrated that integrating Pólya’s method with technological tools can significantly enhance active learning and the development of scientific competencies. For instance, Allison and Joo (2014) adapted this approach to the context of software engineering, showing that its inclusion in the university curriculum not only enabled students to solve well-structured problems but also to tackle wicked problems—those characterized by the complexity of software development. Through a controlled experimental study, they evidenced a significant reduction in problem-solving time and greater effectiveness in solution formulation when students were provided with structured guidance based on Pólya’s model.

Similarly, Chacón-Castro et al. (2023) explored the application of the method in differential equations courses by integrating interactive digital platforms. In this study, students used graphic organizers and mathematical simulators to formulate hypotheses, validate solutions, and represent results. This approach strengthened the connection between theory and practice and facilitated the conceptual understanding of mathematical models.

In the school context, studies such as that of Jahudin and Siew (2024) have shown that the use of Pólya’s model combined with digital representations (such as interactive bar models) enhances algebraic skills in basic education students. Similarly, Klever (2021) pointed out that technology enabled students to visualize the step-by-step problem-solving processes, thereby reinforcing metacognition and reflection—key elements in the final stage of the method.

The actions involved in problem-solving coincide with the basic dimensions of research competence, understood as the ability to formulate questions, inquire into phenomena, design strategies to gather evidence, interpret the obtained data, and communicate results clearly and persuasively. Furthermore, it is highlighted that students who received guidance based on Pólya’s approach demonstrated greater independence, creativity, and clarity in their research reports, confirming its potential as a formative methodology in scientific inquiry processes at early academic stages (Nguyen et al., 2023).

From this perspective, the convergence between Pólya’s problem-solving method and educational technologies could be interpreted as a catalyst for the shift from content-centered teaching to problem-centered learning, supported by digital tools. In this sense, Pólya’s method would appear to offer not only a logical structure for addressing problems but also to serve as a valuable pedagogical mediator when integrated into technological environments, potentially fostering the simultaneous development of cognitive, scientific, and digital competencies in students. Although originally conceived as a heuristic for solving mathematical problems, its sequential structure—understanding the problem, planning a strategy, executing the solution, and reflecting on the process—could closely align with the phases of the scientific research process, particularly in formative learning contexts.

2.3. Technological Resources in Education

Currently, there are commercially available educational technologies, such as the Arduino board and its integrated development environment (IDE) based on the C++ programming language, which are widely used in educational settings for the implementation of projects and technological activities. These devices feature hardware components (sensors, actuators, and specialized boards) designed to address problems linked to local or regional contexts (Fidai et al., 2020).

The development of customized hardware prototypes using boards like Arduino and ESP8266 has proven to be an effective alternative. These prototypes are not only affordable but also support the development of skills such as abstraction, problem-solving, and algorithmic thinking. Previous studies have shown that these solutions are as illustrative as commercial educational technologies and can be complemented with visual programming environments focused on teaching (Fronza et al., 2019; Melo et al., 2020).

The use of these devices encourages experimentation, empirical validation, and the transfer of theoretical knowledge to real-world applications. Through interaction with sensors and actuators, students can observe real phenomena, collect data, design solutions, and evaluate their effectiveness. This process not only strengthens their technical understanding but also enhances their research competencies by encouraging them to formulate hypotheses, analyze results, and propose evidence-based improvements (Suazo, 2014; Trilles et al., 2022).

Electronic devices such as the Arduino board or similar alternatives include a programming environment—a key component that provides the virtual space where students can design, simulate, and control the behavior of electronic devices and sensors (Marín-Marín et al., 2024). The use of visual programming environments such as mBlock or other block-based platforms facilitates the understanding of algorithms and logical structures, even for students without prior experience in traditional programming (Sarmiento, 2020).

These environments foster creativity and abstraction by allowing students to translate ideas and solutions into concrete instructions for the devices. Additionally, they promote the development of computational thinking—a vital cross-cutting skill in today’s context—as it integrates problem decomposition, step-by-step sequencing, and pattern recognition (Llorens et al., 2017).

Block-based programming environments, such as mBlock, Scratch, or AppInventor, have been developed for people of all ages and promote inclusion and diversity in educational contexts (Paucar-Curasma et al., 2025a). These tools have become effective and attractive options for reducing the gender gap in the field of programming and computer science, which has traditionally been male-dominated. By employing a visual approach, these languages facilitate access to programming for young people or individuals with limited reading and writing skills (Paucar-Curasma et al., 2025b; Zhang & Liu, 2018). Likewise, they can adapt to different learning styles and paces, offering the possibility to personalize the experience according to individual needs and interests (Dúo-Terrón, 2023).

In Peru, a STEM (Science, Technology, Engineering, and Mathematics) educational electronic kit, consisting of six thematic electronic boards—agriculture, aquaculture, environment, health, education, and livestock—(PROCIENCIA-CONCYTEC, 2024), was designed and implemented. These boards were created to facilitate the development of investigative activities related to real-world problems in the regional context, incorporating sensors and actuators relevant to each theme (Paucar-Curasma et al., 2022b). Interaction with the STEM educational kit is carried out through a visual block-based programming interface on the mBlock platform, which integrates Python 3.12 libraries and custom blocks to easily control the sensors and actuators of each board. Its visual and intuitive nature motivates students to design contextualized technological solutions, providing immediate feedback during the process. This approach not only promotes the learning of programming but also enhances the logical structuring of problems, creative thinking, and the application of knowledge in real-world settings (Paucar-Curasma et al., 2022a). Figure 1 shows the electronic boards included in the STEM educational kit and the visual programming environment based on mBlock used in its implementation.

2.4. Development of Research Activities Based on Pólya’s Method and the Use of Technological Resources

The development of investigative activities following the problem-solving method proposed by Pólya (1945), complemented with accessible technological resources, constitutes a pedagogical approach that has proven effective in fostering research training at the university level. Previous studies show that its application improves attitudes toward research, strengthens logical thinking, and promotes autonomy in learning (Fronza et al., 2019; Ortega & Asensio, 2021; Paucar-Curasma et al., 2024b). However, most research has focused on specific contexts or particular disciplines, leaving a gap regarding its systematic and comparative implementation among students from different fields—such as engineering and nursing—while integrating educational technologies like electronic boards, STEM kits, sensors, and visual programming. The present study aims to bridge this gap through a proposal that integrates scientific thinking and technological action, guiding students through the four phases of the method—understanding the problem, developing activities, executing activities, and reviewing the solution—to develop research competencies, strengthen autonomy, and promote educational innovation in technology-mediated environments. The following describes the four phases of the proposal:

• Understanding the problem: In this phase, essential research competencies are strengthened in students, as it involves the ability to identify, analyze, and contextualize real-world problems from their surroundings. This process fosters the search for, critical evaluation, and synthesis of relevant scientific and technical information, which constitute a fundamental first step in research training (Espinoza, 2020; Hernández et al., 2020). Moreover, it enhances the development of critical thinking and the formulation of researchable questions—key skills for quality university education and the training of future professionals committed to solving social and health-related issues (Rivas et al., 2020; Turpo-Gebera et al., 2020). This phase ensures that the student is not a passive receiver of knowledge but an active agent capable of interpreting reality and proposing lines of inquiry.
• Designing activities: This phase focuses on the detailed planning of research actions, the search and analysis of background information, and the logical and sequential structuring of tasks. This process strengthens the student’s ability to organize a formative research project methodologically, fostering autonomy and informed decision-making (Llanos de Tarazona, 2019). It also enables future professionals to design projects with a critical and creative focus aligned with the real needs of their environment, which is essential for training in health sciences and for promoting a sustainable research culture (Allison & Joo, 2014; Molina et al., 2020).
• Implementing activities: In this phase, students acquire practical knowledge about the use of technological resources (sensors, electronic kits, visual programming, among others) and apply these tools to solve the identified problem. This process fosters active experimentation, the development of technical skills, and the ability to work collaboratively—fundamental aspects of meaningful learning and the development of practical competencies (Fidai et al., 2020; Paucar-Curasma et al., 2023). It also encourages the creation of functional prototypes and solutions, such as models or interactive applications, which allow students to translate their ideas into tangible products. This strengthens the integration between theory and practice and promotes technological innovation in education (Paucar-Curasma et al., 2023b; Paucar-Curasma et al., 2024a, 2024b).
• Reviewing the solution: In this phase, students conduct a critical analysis of the results and validate the proposed solutions. This process enhances their ability to reflect on procedures and outcomes, identify areas for improvement, and propose optimizations to the designed solutions. In addition, the development of metacognition, understood as the ability to evaluate and improve one’s own research practices (Espinoza, 2020; Flores & Trujillo, 2024), is promoted. This reflective component is essential for consolidating solid research competencies and fostering academic autonomy, preparing students to face future professional challenges critically and adaptively.

Several studies support the effectiveness of this methodology, showing that it improves students’ attitudes toward research, strengthens logical thinking, and fosters autonomy in learning (Fronza et al., 2019; Ortega & Asensio, 2021). Furthermore, applying the proposed method through the use of technological resources facilitates the acquisition of digital competencies, which are crucial in current professional training.

Although numerous studies have confirmed the pedagogical value of Pólya’s problem-solving method in mathematics, computer science, and engineering education, there is still a limited body of research exploring its application in the health sciences. Nursing education, in particular, has traditionally focused on clinical practice and contextual interventions rather than structured methodological frameworks for problem-solving supported by technology. This gap suggests that, despite the recognized potential of Pólya’s model to foster higher-order thinking and reflective practices, its disciplinary transferability has not been sufficiently examined. The present study contributes to bridging this gap by analyzing the implementation of Pólya’s method in two distinct fields—engineering and nursing—through the use of accessible technologies such as Arduino boards, STEM educational kits, and block-based programming environments. In doing so, it expands the theoretical understanding of how problem-solving frameworks can be adapted across disciplinary cultures with different epistemological and professional demands.

3. Materials and Methods

3.1. Research Approach and Participants

This study employed a quasi-experimental pretest–posttest design with a non-probabilistic, purposive sampling strategy, under a quantitative approach. The participants were first-year students from the Systems Engineering and Nursing programs at [blinded for peer-review], respectively. All students were enrolled in either the first or second semester of the 2024 academic year. Most of the participants were under the age of 19 and included both male and female students. They were enrolled in the courses “Formative Research” and “Information Management”.

Table 1. Participants from Peruvian Universities.

University	Program	Male	Female	Total
Universidad Nacional del Centro del Perú	Systems Engineering	28	6	34
Universidad Nacional Autónoma de Tayacaja Daniel Hernández Morillo	Nursing	11	53	64

presents the characteristics of the students from both Peruvian public universities.

The selection of the Systems Engineering and Nursing programs is based on the fact that they represent contrasting disciplinary fields—engineering and health sciences—allowing for the analysis of the effectiveness of Pólya’s method, complemented with technological resources, in educational contexts with different profiles and cognitive demands. Furthermore, in both programs, the need to strengthen research competencies from the early academic cycles was identified. This choice was also facilitated by the fact that one of the co-authors of the research is a faculty member in both programs, which made the coordination, implementation, and monitoring of the educational intervention easier.

3.2. Instrument and Data Analysis

For data collection, a validated instrument from a previous study (Paucar-Curasma, 2023) was used. It was designed to assess research competencies in the context of formative research based on the four phases of Pólya’s problem-solving method. The questionnaire consists of 24 items distributed as follows: understanding the problem (7 items), developing activities (5 items), executing activities (5 items), and reviewing the solution (7 items). Each item is rated on a five-point Likert scale (1 = “never” to 5 = “always”). The instrument was administered in two stages: before (pretest) and after (posttest) the pedagogical intervention.

Data analysis was carried out considering the instruments used and the statistical tests applied to test the research hypotheses. It began with a descriptive analysis using measures of central tendency (mean and median) and dispersion (standard deviation) for each phase, comparing pretest and posttest results. Subsequently, normality was assessed using the Shapiro–Wilk test for Engineering (n < 50) and the Kolmogorov–Smirnov test for Nursing (n ≥ 50). Based on these results, the Student’s t-test for paired samples was applied in Engineering (data with normal distribution), while the non-parametric Wilcoxon signed-rank test was used in Nursing (data with non-normal distribution). In all cases, a significance level of 5% (α = 0.05) was used, rejecting the null hypothesis when the p-value was below this threshold.

3.3. Formative Research Projects in the Classroom

3.3.1. Formative Research Projects for Engineering Students

Table 2. Formative research project proposal and electronic devices.

Formative Research Project	Description	Sensor	Electronic Device
Monitoring of Humidity and Temperature in the Computer Center of the Systems Engineering Faculty	Students used the DHT11 sensor to measure humidity and temperature in the computer center of the university to ensure proper conditions for experiments and research.	DHT11 Sensor	Arduino Board
Air Quality Monitoring in the Market Area of Huancayo City	Students monitored air quality in the market area of Huancayo city using the MQ135 gas sensor to protect public health.	MQ135 Gas Sensor	Arduino Board
Soil Moisture Control to Optimize Corn Cultivation in Cochas District	Students monitored soil moisture in corn fields using the capacitive soil moisture sensor to help farmers make appropriate decisions regarding corn production.	Capacitive Soil Moisture Sensor	Arduino Board
Security Monitoring for Access Control in a Company Library	Students monitored security access to the library, allowing entry only to authorized individuals.	RFID Module RC522	Arduino Board
Water Temperature Monitoring in the Fish Farm Center of Ingenio, Junín	Students implemented a system to monitor water temperature in the fish farm to prevent stress in trout, using the DS18B20 sensor to maintain optimal conditions.	DS18B20 Temperature Sensor	Arduino Board
Monitoring Water Levels of Lake Paca in Jauja Province	Students developed a system to monitor the water level of Lake Paca using an ultrasonic sensor.	HC-SR04 Ultrasonic Sensor	Arduino Board

presents the formative research projects developed in groups by engineering students under the guidance of the classroom instructor. These projects address real-world problems from the students’ own contexts. Each group was assigned an Arduino board and specific sensors to address a particular problem. The sensors used included temperature sensor (DHT11), gas sensor (MQ135), capacitive soil moisture sensor, RFID module RC522, water temperature sensor (DS18B20), and ultrasonic sensor (HC-SR04). To interface the Arduino board with the sensors, the students used a block-based programming environment, mBlock, which enabled them to develop applications focused on monitoring parameters related to community issues.

3.3.2. Formative Research Projects for Nursing Students

Table 3. Formative research project proposal and STEM educational kits.

Formative Research Project	Description	Sensor	STEM Educational Kit
Monitoring Soil Moisture in Vegetable Crops to Prevent Anemia in School-Aged Children in Acraquia District, Tayacaja	The project involved monitoring soil moisture in vegetable crops to help prevent anemia in children, using the agriculture card, capacitive moisture sensor, and mBlock environment.	Capacitive Moisture Sensor	Agriculture Card
Air Quality Monitoring in Wood-Fired Households to Prevent Respiratory Problems in Andaymarca, Tayacaja	The project monitored air quality in wood-fired households using the environment card, air quality sensor, and mBlock programming to prevent respiratory issues.	MQ135 Air Quality Sensor	Environment Card
Monitoring Guinea Pig Breeding to Prevent Salmonella in Santa Rosa Community, Tayacaja	The project focused on monitoring guinea pig breeding to prevent contamination and human salmonella infection, using the livestock card, distance sensor, and mBlock programming.	HC-SR04 Distance Sensor	Livestock Card
Water Quality Monitoring to Prevent Stomach Infections in Ustuna District, Tayacaja	This project monitored water quality to prevent gastrointestinal infections, using the aquaculture card, turbidity sensor, and mBlock environment.	Water Turbidity Sensor	Aquaculture Card
Monitoring Water Temperature in “La Cabaña” Fish Farm to Avoid Trout Mortality and Contaminated Consumption	The project monitored fish farm water temperature to prevent trout mortality and human consumption of contaminated meat, using the aquaculture card, temperature sensor, and mBlock.	DS18B20 Water Temperature Sensor	Aquaculture Card
Monitoring Children’s Body Temperature to Prevent Fever Outbreaks in Mariscal Cáceres School, Tayacaja	The project monitored children’s body temperature to prevent fever outbreaks using the health card, body temperature sensor, and mBlock programming environment.	MLX90614 Body Temperature Sensor	Health Card

shows the formative research projects developed in groups by nursing students, guided by the course instructor. Each project addressed a health-related problem identified within the local context of the students, allowing for a more meaningful and contextualized research activity. Each group was assigned a specific card from the STEM educational kit, equipped with appropriate sensors, to carry out investigative activities using educational technologies.

During project implementation, different cards from the kit were used: the green card (agriculture) with a soil moisture sensor; the black card (environment) with an air quality sensor (MQ135); the yellow card (livestock) with an ultrasonic sensor (HC-SR04); the blue card (aquaculture) with water turbidity and water temperature sensors (DS18B20); and the red card (health) with a body temperature sensor (MLX90614).

All the electronic cards were programmed using the visual block-based programming environment mBlock, facilitating sensor interaction and allowing students to develop technological solutions tailored to their identified health issues.

3.4. Development of Investigative Activities Following Pólya’s Method

Once the formative research projects were assigned, engineering and nursing students, under the supervision of classroom instructors, began developing their investigative activities according to the established schedule based on the four problem-solving phases: understanding the problem, planning activities, executing activities, and reviewing the solution. Figure 2 presents the schedule along with the technological resources used at each stage, allowing for a clear visualization of the sequence and organization of classroom activities.

The following section describes the activities carried out by engineering and nursing students following the four phases of problem-solving. As illustrative examples, the activities related to the formative research projects titled “Monitoring Water Temperature in the Fish Farming Center of Ingenio in the Junín Region” (engineering students) and “Monitoring Air Quality in Households Using Firewood Stoves to Prevent Respiratory Problems in the District of Andaymarca, Province of Tayacaja” (nursing students) are presented.

(a)

Understanding the problem (5 sessions).

In this phase, students engaged in various activities aimed at gaining an in-depth understanding of the research topic assigned. Key actions included searching for information using artificial intelligence applications (such as ChatGPT) and academic search engines (Google Scholar, Scopus, SciELO), among other resources. Subsequently, they carried out an analysis and synthesis process of the collected information, from which they prepared a one-page document with scientific citations managed through bibliographic tools such as Mendeley, describing the problematic situation. In addition, they represented the cause-and-effect relationships of the issue through a visual organizer, which allowed them to better structure their ideas and achieve a deeper understanding of the proposed topic. Figure 3 shows an example of the visual representation of cause and effect related to formative research projects in the Engineering and Nursing programs.

(b)

Designing activities (3 sessions).

In this phase, students researched the background related to their selected research topics. To do so, they consulted various scientific sources, such as Scopus and Google Scholar, among others. Once the information was collected, they proceeded to analyze it and identify similar experiences or activities that had already been carried out. Based on this analysis, they developed a list of activities aimed at proposing viable solutions to the stated problem. These activities were designed according to the local context, technical feasibility, and the use of the Arduino board and the STEM educational kit.

Table 4. List of activities proposed by engineering and nursing students.

Engineering

Nursing

− Formulate the problem related to trout farming and water temperature; − Search for background and previous models as reference; − Build the first prototype using the DS18B20 water temperature sensor and Arduino Uno board; − Develop the initial programming using the mBlock application; − Review and adjust the prototype using mBlock programming; − Review and correct the programming using the mBlock application; − Final review and correction of the prototype and programming in mBlock; − Build a model inspired by the research topic. − Prepare a scientific article.

− Identify and understand the issue of air pollution caused by firewood stoves in the district of Andaymarca; − Design the circuit using the environment card and the MQ135 air quality sensor; − Identify and acquire parameters related to Air Quality Index (AQI); − Develop an application for air quality monitoring using the mBlock program to display the AQI; − Build a prototype simulating a home using firewood stoves for air quality measurements; − Prepare a scientific article.

presents the set of activities proposed by the engineering and nursing students.

(c)

Implementing activities (6 sessions).

In this stage, students addressed the stated problem by implementing the activities they had previously designed. As a first step, they became familiar with the operation of the Arduino board, the educational cards from the STEM kit, sensors, and actuators, following a laboratory guide prepared for this purpose. Once they acquired the basic knowledge, they proceeded to assemble the circuits and connect them to their laptops; then, they developed applications using the visual programming environment mBlock, aiming to monitor various parameters.

Additionally, students built a physical model in which they simulated the solution to the problem, integrating the electronic cards with sensors and the application developed in mBlock. They also wrote a scientific article that captured the information gathered during the experience.

Figure 4 illustrates the programming logic in mBlock developed by the engineering students, linked to a water temperature sensor in a trout farming pond. The system associates temperature values with messages, animations, and visual responses representing the biological behavior of trout. The program classifies temperature into three critical ranges (≤8 °C, 9–21 °C, and ≥22 °C), displaying specific animations for each range to depict stages such as reproduction, growth, or stress. These animations are accompanied by key messages, such as “eggs”, “fry”, “aero”, and “extinction”. This logic allows for an educational and visual simulation of the impact of thermal variations on the trout life cycle, as well as the importance of implementing intervention measures—such as water oxygenation—thus fostering awareness of environmental management in aquaculture.

Figure 5 shows the programming logic in mBlock developed by nursing students, linked to the air quality sensor. The system associates Air Quality Index (AQI) values with messages and visual responses. The program classifies pollution levels into six AQI intervals (≤600, 601–700, 701–800, 801–900, 901–1000, and ≥1001 ppm) and, for each range, displays animated alert messages accompanied by graphic elements—such as a child, a nurse, a mother, and smoke particles—that illustrate the community health consequences resulting from variations in smoke concentration.

Regarding the development of the application interface in mBlock, for the case of engineering students, Figure 6a presents one of the three interactive screens designed to illustrate the effects of water temperature on the well-being of trout in a fish farm. In the example, the screen is activated when the sensor detects a temperature of 26 °C, a value considered critical as it causes thermal stress in trout. Under this condition, the virtual environment adopts a reddish tone, the fish exhibit agitated movements as a warning sign, and bacteria such as Aeromonas appear. This interface facilitates the understanding of the consequences of temperature increase in the fish farm, highlighting the importance of real-time environmental monitoring.

In the case of nursing students, Figure 6b shows one of the six interactive screens developed in mBlock to communicate the health risks associated with smoking, incorporating graphic elements, text, sound, and colors. In the example, the screen is activated when the sensor records air quality levels between 600 and 700 ppm. In this situation, the nurse displays the message “mild discomfort in sensitive groups”, and a yellow light turns on, indicating moderately good air quality. Visually, the mother and child maintain expressions of calmness, while the nurse adopts an attentive posture, emphasizing the onset of a precautionary condition.

(d)

Review of the solution (2 sessions).

In this phase, engineering and nursing students verified the results obtained from the investigative activities developed in the classroom. They assessed the performance of the constructed models after integrating the Arduino board and the STEM educational electronic cards, and interacting with the application created in mBlock. Subsequently, they optimized their solutions based on the observations and suggestions provided by the instructor and completed the writing of their research article.

Figure 7a presents the simulation of a scaled fish farm used to verify the system’s response to three critical temperature ranges (≤8 °C, 9–21 °C, and ≥22 °C). In each interval, dynamic visual changes were triggered in the fish costumes, along with movement animations and informative messages regarding their biological status (reproduction, growth, or stress). This validation confirmed that the sensor readings and programming logic functioned correctly, clearly and accurately representing the effects of temperature on the fish in a simulated environment.

Figure 7b shows the simulation of a wood-burning kitchen model, where the system’s response was evaluated under different smoke concentration levels. The interactive screens displayed the recorded levels in ppm units, classifying the Air Quality Index (AQI) into six intervals: ≤600, 601–700, 701–800, 801–900, 901–1000, and ≥1001 ppm. For each range, the system activated animated alert messages and differentiated visualizations representing progressive effects on human health. Risk communication was conveyed through characters (child, mother, and nurse), alert colors, texts, and icons, facilitating the interpretation of the exposure level.

4. Results

4.1. Descriptive Evaluation of Research Competence According to the Problem-Solving Phases

Table 5. Summary of the statistical analysis.

Phases of the Problem-Solving Method	Engineering						Nursing
	Mean		Median		Standard Deviation		Mean		Median		Standard Deviation
	Pre Test	Pos Test	Pre Test	Pos Test	Pre Test	Pos Test	Pre Test	Pos Test	Pre Test	Pre Test	Pos Test	Pre Test
Understanding the problem	25.0	26.6	25.0	26.0	3.57	3.62	4.31	4. 45	4.00	4.31	4. 45	4.00
Designing activities	16.9	17.9	17.0	17.5	2.34	2.64	3.50	3.84	3.00	3.50	3.84	3.00
Implementing activities	19.0	19.6	19.0	19.5	2.09	2.67	4.19	4.25	4.00	4.19	4.25	4.00
Reviewing the solution	24.7	26.9	24.5	28.0	2.95	3.61	4.05	4.30	4.00	4.05	4.30	4.00

presents the statistical summary of research competence scores within the context of formative research, organized according to the phases of the problem-solving method, for engineering and nursing students. The results show an increase in both mean and median values after the intervention, indicating an overall improvement across all evaluated phases. Likewise, in most cases, a decrease in the standard deviation was observed, reflecting greater consistency in the participants’ responses.

4.2. Normality Test of Collected Data

According to

Table 6. Shapiro–Wilk/Kolmogorov–Smirnov normality test.

	Engineering		Nursing
	Pre-Test	Post-Test	PRE-TEST	Post-Test
Test	Shapiro–Wilk		Kolmogorov–Smirnov
N	34	34	64	64
W/Statistic	0.983	0.991	0.361	0.218
p-value	0.864	0.993	<0.001	<0.002

, for engineering students, the Shapiro–Wilk test showed that the pretest (p = 0.864) and posttest (p = 0.993) scores did not deviate from a normal distribution. In the case of nursing, the Kolmogorov–Smirnov test indicated that the pretest (p < 0.001) and posttest (p < 0.002) scores did not follow a normal distribution. Based on these results, the paired Student’s t-test was applied to the engineering group, while the Wilcoxon signed-rank test was used for the nursing group, in order to assess the significance of changes between pretest and posttest scores.

4.3. Hypothesis Testing of Research Competence According to the Problem-Solving Phases

In engineering, the paired Student’s t-test was applied, while in nursing, the non-parametric Wilcoxon signed-rank test was used. In both groups, statistically significant differences were found between pretest and posttest scores in all phases of the problem-solving method (p < 0.05), indicating a positive effect of the intervention.

Table 7. Hypothesis testing results for research competence according to problem-solving phases.

Problem-Solving Phases	Engineering		Nursing
	H0 = “The implementation of a problem-solving method based on Pólya’s approach and complemented with an Arduino board and sensors does not contribute to strengthening research competences in engineering students”.		H0 = “The implementation of a problem-solving method based on Pólya’s approach and complemented with an electronic STEM educational kit does not contribute to strengthening research competences in nursing students”.
	H1 = “The implementation of a problem-solving method based on Pólya’s approach and complemented with an Arduino board and sensors contributes to strengthening research competencies in engineering students”.		H1 = “The implementation of a problem-solving method based on Pólya’s approach and complemented with an electronic STEM educational kit contributes to strengthening research competences in nursing students”.
	Significance level: 5% Decision: if p ≥ 0.05 → do not reject H0; if p < 0.05 → reject H0		Significance level: 5% Decision: if p ≥ 0.05 → do not reject H0; if p < 0.05 → reject H0
	p-Value (Student’s t-Test)	Decision	p-Value (Student’s Wilcoxon)	Decision
Understanding the problem	0.022	Reject H0	0.001	Reject H0
Designing activities	0.021	Reject H0	0.002	Reject H0
Implementing activities	0.041	Reject H0	0.002	Reject H0
Reviewing the solution	0.001	Reject H0	0.006	Reject H0

presents the results of the hypothesis tests for engineering and nursing students.

In all evaluated phases, statistically significant differences were found between the pretest and posttest in both engineering (Student’s t-test, p < 0.05) and nursing (Wilcoxon test, p < 0.05), indicating that the educational intervention generated substantial improvements in the research competencies of both groups. These results support the effectiveness of the implemented methodological approach in fostering critical analysis, autonomous planning, the application of technological resources, and reflective evaluation among first-year university students.

4.4. Analysis of the Development of Research Competence According to the Phases of Problem-Solving and Academic Program

The results in Figure 8 show progress in the research competencies of engineering students after applying the 4-phase problem-solving method using technological resources such as the Arduino board, sensors, and mBlock.

In the problem comprehension phase, the “very high” level increased from 6% (pre) to 23% (post), while the “high” level decreased from 62% to 56%. The “very low” level disappeared, and the “neutral” level dropped from 32% to 21%.

In the activity planning phase, the “very high” level rose from 6% to 20%, while the “high” level decreased from 79% to 62%. The “neutral” level slightly decreased, and the “low” level remained at 3%.

During the activity execution phase, the “very high” level increased from 3% to 12%, and the “high” level increased from 32% to 38%. Meanwhile, the “neutral” level decreased from 59% to 44%, and the “low” level remained unchanged.

Finally, in the solution review phase, the “high” and “very high” levels rose from 53% to 80%, with the “neutral” level decreasing from 44% to 20%, reflecting improvements in critical thinking and autonomous evaluation, and the “very low” level disappeared.

The results in Figure 9 show progress in the research competencies of nursing students after applying the problem-solving method supported by an educational STEM kit. The analysis focused on the phases of problem comprehension, activity planning, activity execution, and solution review.

In problem comprehension, the “high” and “very high” levels increased from 45% and 6% to 56% and 38%, respectively; the “neutral” level dropped from 45% to 6%, and the “low” level disappeared.

In activity planning, the upper levels “high” and “very high” rose from 42% and 6% to 45% and 19%, respectively; the “neutral” level decreased from 47% to 33%, and the “low” level dropped from 5% to 2%.

In activity execution, the “very high” level increased from 35% to 41%, while the “high” level decreased slightly from 47% to 44%; the “neutral” level remained almost the same at 16%.

Finally, in solution review, the “very high” level increased from 31% to 44%, the “high” level remained unchanged, and the “neutral” level decreased from 27% to 14%, reflecting improvements in critical thinking and self-evaluation.

Figure 10 displays the percentage improvement in the “High” and “Very High” levels for each problem-solving phase across engineering and nursing programs. The graph demonstrates that both groups achieved significant progress in their research competencies after the educational intervention based on Pólya’s problem-solving method supported by technology (Arduino board and STEM educational kit). There is no exclusive distinction by discipline, as both groups improved across all evaluated phases. Notably, in the implementation phase, nursing students reached a 50% increase in the “High” and “Very High” levels, highlighting a strong engagement with the technological STEM kit. Meanwhile, engineering students exhibited consistent improvements, particularly in the solution review phase, reflecting a strengthened critical thinking ability and reflective evaluation. These findings support the effectiveness of the proposed methodological approach and its cross-disciplinary applicability in diverse formative contexts.

5. Discussion

The findings of this study confirm that the implementation of a problem-solving method based on Pólya’s framework, combined with the use of technological tools (Arduino board, STEM educational kit, and mBlock), had a positive effect on the development of research competencies in engineering and nursing students from two public universities in Peru. This methodological approach enabled the structuring of the research process into four well-defined phases—understanding the problem, designing activities, implementing activities, and reviewing the solution—thus providing a clear and guided didactic framework for students in the early stages of their university education (Fernández & Duarte, 2013; Pinto & Cortés, 2017).

The descriptive and comparative results show substantial increases in the “high” and “very high” levels across all evaluated phases for both degree programs, with particularly notable gains in activity execution for nursing and in solution review for engineering. This suggests that, while the implemented approach generally strengthens research competencies, its impact varies according to the discipline and phase of the process. In nursing, the greatest improvement during activity execution is likely due to the applied and contextualized nature of the interventions, which require transferring theoretical knowledge to real health scenarios, enhancing decision-making and direct action (Magdaleno & Malaga, 2024). In engineering, the largest progress in solution review is linked to the need for technical and analytical validation of results, demanding rigorous critical evaluation and process adjustments (Jiménez et al., 2020). These findings, confirmed as statistically significant through the Wilcoxon test and Student’s t-test (p < 0.05), support the effectiveness of the approach in fostering critical analysis, autonomous planning, technological application, and reflective evaluation (Karmawan & Djamilah, 2024; Neo et al., 2021; Pluhár & Torma, 2019).

In the case of nursing, the integration of the STEM educational kit with sensors, electronic boards, and mBlock visual programming allowed students to develop functional prototypes addressing real health issues in their communities. This promoted scientific inquiry from a contextualized and socially relevant perspective (Paucar-Curasma et al., 2023b). Meanwhile, engineering students developed applications focused on environmental monitoring and technological processes, such as thermal control in fish farms, demonstrating the versatility of the approach across disciplines.

It is worth noting that a slight decrease in the combined “high” and “very high” level was observed in the activity design phase for engineering; however, there was an increase in the “very high” category. This suggests a qualitative improvement among a group of students and a critical reevaluation of their performance by others, possibly influenced by the higher level of rigor and metacognitive awareness acquired during the intervention (Alejandro & Zamora, 2024; Barrios Soto & Delgado González, 2021).

Overall, these results indicate that the problem-solving-based methodological approach, complemented by accessible technological tools, is effective and broadly applicable, regardless of the academic field. The experience demonstrates that, both in health and technological contexts, students can strengthen their research competencies through active and technology-supported methodologies aligned with principles of equity, inclusion, and sustainability (Fronza et al., 2019; Molina et al., 2020; Ortega & Asensio, 2021).

The observed disciplinary variability highlights important theoretical implications. Engineering students demonstrated greater progress in the solution review phase, which can be explained by their prior familiarity with technological tools and their inclination toward analytical validation of results (Urquizo et al., 2021; Wu et al., 2019). In contrast, nursing students achieved more substantial gains in the execution phase, reflecting their ability to transfer theoretical knowledge into practical, health-related scenarios (Rossi-Rivero & Padilla-Choperena, 2019). These findings suggest that Pólya’s problem-solving method does not operate uniformly across disciplines but rather adapts to the epistemic culture of each field (Allison & Joo, 2014; Chacón-Castro et al., 2023; Jahudin & Siew, 2024). From a theoretical standpoint, this implies that Pólya’s four-phase model should be understood not as a rigid sequence but as a flexible framework whose emphasis varies depending on disciplinary orientation (Olaniyan & Govender, 2018; Öndeş, 2025). This disciplinary adaptability enhances the explanatory power of the model, positioning it as a transferable methodology capable of fostering research competence in both technical and health sciences while acknowledging their distinctive learning trajectories (Paucar-Curasma et al., 2024a).

The use of technological resources—such as Arduino boards, STEM educational cards, electronic sensors, actuators, and programming environments like mBlock—combined with problem-solving-centered methodologies, fosters the development of professionals with critical thinking, creativity, and the ability to propose technological solutions and evaluate results. This educational combination supports the development of transversal competencies that enable students, regardless of their discipline, to effectively face social, environmental, health, and other challenges in their communities (Rossi-Rivero & Padilla-Choperena, 2019; Suárez et al., 2022).

Furthermore, this educational intervention contributes to the achievement of the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities) (UNESCO, 2020), by providing meaningful learning opportunities through technology, even in vulnerable regions (Boakye et al., 2024; Müller et al., 2025).

6. Conclusions

The implementation of the problem-solving method based on Pólya’s proposal, structured into four phases, has proven to be an effective pedagogical strategy for strengthening research competencies in engineering and nursing students at public universities in Peru. The integration of technological resources, such as the Arduino board, the STEM educational kit with sensors, and visual programming in mBlock, facilitated the development of contextualized projects that not only fostered active learning but also promoted autonomy, critical thinking, and the transfer of knowledge to real-world contexts.

The findings are particularly relevant in the educational context of Peru and Latin America, where challenges persist regarding equity in access to educational technologies and the need for methodologies that connect academic knowledge with real-life problems. The experience shows that, while both fields demonstrated significant progress in all phases, the most marked differences—activity execution in nursing and solution review in engineering—may be related to baseline skills and the level of technological familiarity of each group. Engineering students generally have greater prior experience in handling technological tools, which supports more detailed analysis during evaluation and adjustment phases; whereas nursing students, by working in community health scenarios, tend to enhance their competencies during the practical execution of activities.

Nevertheless, both groups developed transversal skills such as collaborative work, scientific communication, and decision-making, confirming that the adoption of educational technologies applied to formative research is not limited to a specific professional field. This methodological approach represents a replicable and scalable alternative in Latin American higher education, with potential for adaptation across disciplines, strengthening the training of professionals capable of addressing real problems from a scientific, ethical, and technological perspective.

The variability observed across problem-solving phases suggests that the integration of Pólya’s method with technological resources constitutes a flexible and adaptive pedagogical framework. Its ability to generate differentiated impacts in engineering and nursing highlights its cross-disciplinary applicability, reinforcing its theoretical validity as a model for fostering research competence in diverse academic domains.

Finally, by promoting meaningful, technological, and contextualized learning, this approach contributes to the construction of more resilient and inclusive educational systems, capable of responding with social relevance to the challenges of the environment and advancing toward equitable, ethical, and sustainable professional training.