Buele, Jorge
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Buele, Jorge
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Ambato
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jorgebuele@uti.edu.ec
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57200380202

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Analyzing barriers to the effective implementation of technological tools in inclusive education: a scoping review

2025 , Humberto Murillo-Jiménez , Marco Centeno-Alarcón , Buele, Jorge , Francisco Yumbla

Introduction: Digital accessibility and inclusive pedagogy are central to achieving equitable education systems worldwide. Yet, the integration of technological tools for students with disabilities remains inconsistent, often limited to fragmented initiatives without long-term institutional or policy support. Understanding the structural barriers that constrain digital inclusion is crucial for transforming technology into an enabler of educational equity rather than a source of further exclusion. Methods: This study conducted a scoping review following PRISMA-ScR guidelines to identify and analyze barriers affecting the implementation of educational technologies in inclusive education. A systematic search across six databases (ERIC, Scopus, ACM Digital Library, EBSCOhost, Wiley Online Library, and Web of Science) yielded nine primary studies published between 2015 and 2025. Data were thematically synthesized through inductive–deductive coding to identify recurring structural, pedagogical, and policy patterns. Results: The findings reveal persistent deficits in teacher training and digital competence, technological and infrastructural limitations, economic constraints, and weak enforcement of inclusion policies. Additionally, attitudinal barriers, including low expectations toward students with disabilities and limited institutional accountability, hinder sustainable progress. Positive factors, such as teacher initiative, institutional commitment, and universal design-based practices, partially mitigate these challenges, demonstrating the potential of inclusive technologies when supported by coherent policy and training structures. Conclusion: Ensuring genuine digital inclusion requires embedding accessibility and universal design as structural components of education systems. Sustainable progress depends on coordinated governance, investment, and professional development that bridge the gap between policy and classroom practice. Registration: This review was registered in Open Science Framework: 10.17605/OSF.IO/T5K7Y. Copyright © 2025 Murillo-Jiménez, Centeno-Alarcón, Buele and Yumbla.

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Strategic Planning to Improve the Production Systems of an MSME: Case Study of a Toy Store

2024 , Buele, Jorge , Lozada-Cepeda J.A. , Ruales, María Belén

Competitiveness in the sales and services sector is becoming more and more intense. Therefore, the implementation of methodologies and strategies is necessary for the achievement of objectives, business growth, and the improvement of production systems. This article presents the performance of a strategic plan (SP) to identify the shortcomings of a company belonging to the MSME group. A toy store developing in Ecuadorian business is presented as a case study. The initial analysis uses tools such as the external PESTEL analysis, the Servqual model, and an analysis of current competitiveness. A new business model (Business Model Canvas) is defined to implement the plan, and strategic initiatives and projects are established. Since this study is projected for five years, experimental results are obtained for the first year, which shows a reduction in the gaps between customer expectations and the service offered. There is also evidence of minor quality mismatches in the Servqual model and a better competitive position than others. These objectives are related to increasing the company’s performance and combining quality, cost, and logistics on time. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Augmented Reality Application with Multimedia Content to Support Primary Education

2023 , Buele, Jorge , Espinoza J. , Ruales, María Belén , Camino-Morejón V.M. , Ayala-Chauvin, Manuel Ignacio

Education is continually reinventing itself to meet the growing needs of learners. In the context of an emergency, confinement and mobilization difficulties have necessitated the adoption of online education. This new offer brings technological means to conventional processes, but its main disadvantage is the limited access in some places. This has motivated the present study, which proposes the development of a mobile application using augmented reality (AR) to complement primary education. This application aims to disseminate multimedia content without needing a stable internet connection. A high-performance computer is required for the design and a mid-range smartphone for its execution. Four scripts are generated in c# programming language using Visual Studio. The environment and the three-dimensional objects are developed using Unity software and the packages ARFoundation, Unity MARS, and Vuforia. Two groups of 13 children each participated in the experimental testing. The experimental group used the application for two weeks to complement the virtual classes in the subjects of language, natural sciences, and mathematics. The analysis with SPSS software shows a statistically significant increase in the average grades compared to the control group. This research shows that the use of technology can contribute to improving the current teaching-learning processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Predictive Maintenance in Industrial Robotics Using Big Data: Techniques, Challenges, and Opportunities

2024 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo, Fátima , Dayanara Yánez-Arcos , Buele, Jorge

In industrial robotics, predictive maintenance is important to improve efficiency and reduce costs, addressing early detection and diagnosis of failures. The use of Big Data allows us to identify patterns and trends that at first glance are complex. This review examines research on the application of big data in predictive maintenance of industrial robots, which use advanced techniques such as cloud-based architectures, filtering algorithms, and machine learning. The review methodology included an analysis of the big data techniques used, the challenges identified, and the opportunities presented. The results show significant improvements in the accuracy of predictions and fault diagnoses. Key anomaly drivers were identified that improved production performance and enabled accurate fault identification and reduced downtime in industrial robots. Despite the benefits, challenges remain in data security and communications latency, underscoring the need to develop innovative algorithms and techniques to balance computing load and minimize delays. The continuous evolution of these techniques promises to improve the failure management capacity in industrial robotics, thus optimizing the operability and efficiency of robotic systems.

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Adaptive Learning Platforms and Their Influence on Higher Education: A Scoping Review

2025 , Núñez Hernández, Corina , Avilés-Castillo, Fátima , Buele, Jorge

Over the past decade, adaptive learning platforms have transformed higher education by adjusting content and activities based on students’ abilities and progress. This approach personalizes learning and optimizes academic performance, particularly in complex disciplines such as engineering and science. However, challenges remain related to their technological implementation and acceptance by both students and educators. This scoping review analyzes the impact of adaptive learning platforms in higher education, focusing on their influence on academic performance, student motivation, practical implementation challenges, and future implications for research. An exploratory literature review was conducted using the PRISMA-ScR method, identifying recent studies published between 2019 and 2024 in databases such as ERIC, Scopus, IEEE Xplore, Web of Science, and APA PsycNet. Quantitative and qualitative research studies were selected, evaluating the impact of these platforms in university settings and the outcomes they produce. The results indicate that adaptive learning platforms significantly enhance academic performance by providing personalized experiences and immediate feedback. They also increase student motivation and engagement, particularly in online learning environments. However, challenges were identified, including the need for adequate technological infrastructure and specialized training for educators. Adaptive learning platforms hold transformative potential in higher education but require overcoming technological and pedagogical barriers. Further qualitative research is recommended to better understand user experiences and maximize their effectiveness across diverse educational contexts.

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Defect Reduction in Textile Manufacturing: A Review

2025 , Ángeles Solís-Solís , Sebastián Villacís-Capuz , Marco Centeno-Alarcón , Avilés-Castillo, Fátima , Buele, Jorge

The textile industry faces continuous challenges in reducing defects during manufacturing, as quality inconsistencies can lead to financial losses, reduced efficiency, and diminished consumer confidence. Addressing this issue requires a combination of methodological and technological approaches that optimize production while minimizing waste. This review examines various strategies for defect reduction, focusing on process improvement methodologies, predictive technologies, and sustainable practices. A systematic analysis of 12 studies highlights the effectiveness of structured quality management approaches in identifying and eliminating defect sources. Additionally, predictive models based on artificial intelligence have demonstrated significant potential in real-time defect detection and prevention, improving overall product quality. However, challenges such as machinery rigidity and the high variability of textile products complicate the implementation of these strategies. Sustainable manufacturing practices and specialized workforce training have also been identified as key factors in enhancing defect management, as they contribute to resource conservation, waste reduction, and improved working conditions. The findings suggest that a comprehensive approach, integrating advanced process optimization techniques, predictive analytics, and sustainable production methods, is essential for improving efficiency and quality in textile manufacturing. This integration not only reduces defects but also strengthens competitiveness in an industry increasingly driven by quality, sustainability, and technological innovation. The OSF registration can be accessed at: https://doi.org/10.17605/OSF.IO/NAMWY. © 2025 IEEE.

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Design and validation of the aggression tendency evaluation questionnaire (CETA-18)

2026 , Pérez Vega, Doris , Subia Arellano, Andrés , Luis Carlos Jaume , Buele, Jorge

Aggressive behavior remains a critical concern in contemporary society, with individuals frequently exhibiting disproportionate responses to frustration or perceived provocation. Given the social and psychological consequences of such behavior, the present study aimed to develop and validate the Aggression Tendency Evaluation Questionnaire, a brief self-report scale designed to assess individual predisposition to aggression. The instrument was applied to a sample of 740 adults (365 men, 375 women) aged 18 to 70, residing in Quito, Ecuador. A quantitative, non-experimental, cross-sectional design was employed, and data were analyzed using Cronbach’s alpha, the Kaiser–Meyer–Olkin measure, Bartlett’s test of sphericity, exploratory factor analysis, and Spearman’s correlation. The questionnaire demonstrated high internal consistency (α = 0.904) and a clear three-factor structure, identifying verbal-expressive aggression, social/indirect aggression, and physical-reactive aggression as distinct dimensions. Correlational analyses revealed weak but significant inverse associations between aggression tendency and variables such as age, employment status, and religious practice, suggesting these factors may serve as protective elements. Other variables, including gender, educational level, and relationship status, showed no significant associations. These results support the questionnaire as a valid and reliable tool for assessing aggression tendency in adult populations. Its psychometric robustness and ease of application make it a practical instrument for use in both research and applied psychological contexts. Further studies are recommended to confirm its factorial structure and explore its cross-cultural applicability.

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Implementation of an electro-optical sensor in the twin otter fae plane for the strengthening of the strategic surveillance capacity [Implementación de un sensor electro-óptico en el avión twin otter fae para el fortalecimiento de la capacidad estratégica de vigilancia]

2020 , León G. , Enríquez V. , Salazar F.W. , Guallo J.F. , Urrutia F. , Buele, Jorge

In this research work, a feasibility study is presented in order to install an electro-optical sensor on the Twin Otter DHC-6 aircraft of the Ecuadorian Air Force, to strengthen the strategic capacity of surveillance, reconnaissance and intelligence. The study took into consideration the limitations and restrictions inherent to the aircraft, which is why it was decided to make a temporary, non-invasive and easy to assemble installation. For the study of affectation, the aerodynamic, structural analysis, loads, weight and balance were considered. Mechanical components and a fairing have been designed in composite materials, so that the sensor attachment is safe and has an aerodynamic surface that reduces the drag coefficient generated. To validate this prototype, a comparison of the aircraft in its normal state with respect to the adhesion of this device is made through tests and simulations. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

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Cognitive Flexibility and Attitude Toward AI: A Correlational Study

2025 , Subia Arellano, Andrés , Pérez Vega, Doris , Rocio Patiño-Fernández , Buele, Jorge

Artificial intelligence plays a leading role across various sectors, underscoring the importance of understanding the individual factors influencing their acceptance. Previous research has pointed out that variables such as age, gender, and cognitive flexibility impact attitudes toward these technologies. However, the interaction among these variables still requires further analysis. This study sought to explore the relationships between cognitive flexibility, age, gender, and attitudes toward artificial intelligence in a sample of 342 participants, with an average age of 26.80 years. Employing a descriptive-correlational design, two scales were used: one to measure cognitive flexibility and another to assess attitudes toward this technology. Due to the lack of normality in the distributions of the variables, Spearman's correlation was used for the analysis. The results show that cognitive flexibility and educational level have a positive and significant relationship with the attitude toward artificial intelligences (r = 0.245, p < 0.001 and r = 0.140, p = 0.009, respectively). On the other hand, age presents a weak negative relationship (r = -0.117, p < 0.05), while no significant relationship was observed with gender. These findings provide an initial basis for understanding individual differences in technology acceptance, although further research is needed to delve into the underlying mechanisms and evaluate other contextual factors.

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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms

2022 , Espinosa Pinos, Carlos Alberto , Ayala-Chauvin, Manuel Ignacio , Buele, Jorge

Several factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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