Serie Científica de la Universidad de las Ciencias Informáticas

Workflow for effective integration of community detection algorithms in brain network analysis

Flavio Averhoff — 2025-07-01

This study presents a workflow utilizing network analysis based on community detection methods and functional magnetic resonance imaging (fMRI) to investigate brain connectomics problems. The objective of the study is to enhance the understanding of brain architecture and its clinical implications, particularly in identifying altered connectivity patterns in neurological and psychiatric conditions. Techniques such as intensity normalization and image smoothing were applied to ensure the quality of fMRI data processing. An autoencoder model was employed to analyze functional connectivity networks, and the Louvain algorithm was used to detect communities within these networks. High modularity values were achieved, and validation tests confirmed the robustness of the algorithm used in the analysis. This study advances our understanding of brain architecture and has significant clinical implications by identifying altered connectivity patterns, which may improve the diagnosis and treatment of neurological and psychiatric conditions.

Computational model for the evaluation of the development of academic writing skills in the Curricular Integration Unit subject

Dionisio Vitalio Ponce Ruiz — 2025-07-01

A computational model was developed to assess the development of academic writing skills in the Curricular Integration Unit course. The research aimed to design a system that measures and improves research writing skills, promoting effective training for student researchers in the Law program at the Quevedo Extension. The intervention consisted of implementing the model in a case study with eighth-semester Law students at the Universidad Regional Autónoma de los Andes, Quevedo Extension. The computational model was validated using performance metrics such as accuracy, number of errors, and consistency in textual production, proving effective in identifying areas for improvement and guiding pedagogical strategies. The results show that the model significantly contributes to enhancing academic writing skills, thus improving students' research competencies in producing scientific articles. The model was shown to be useful and aligned with the training objectives, highlighting its potential for implementation in other similar subjects and contexts. However, it is recommended to expand the study with a more diverse group of students to strengthen the generalizability of the results and further optimize the system.

Decisional information system on training load in sports medical control

Jonathan González Pieras — 2025-07-01

This research focuses on the development and implementation of a decisional information system that supports sports medical monitoring by optimizing training load. The study highlights the construction of an inference engine that uses case-based reasoning, aimed at managing the process of selection, decision, interpretation, and application of behavior that emulates human reasoning. This engine processes and interprets specific rules and established premises to solve decision-making problems, allowing the evaluation of various options and the selection of the most appropriate alternative for each particular situation. The research combines a comprehensive literature review with empirical methods, validating the usefulness and efficiency of this approach in physical load monitoring contexts. The technological and methodological integration of the system is emphasized, highlighting its ability to automate information processing and provide precise support in the decision-making process. The methodological approach focuses on the precision of the implemented rules and the inference engine's ability to adapt to various conditions and criteria specific to the sports environment. In essence, the research concludes that the implementation of this information system, with its case-based inference engine, represents a comprehensive and effective tool for improving decision-making in sports medical monitoring, ensuring that the training load is tailored to the individual needs of each athlete.

Computational analysis of basic English vocabulary learning for General Basic Secondary Education

Luis Orlando Albarracín Zambrano — 2025-07-01

A computational model was designed to improve basic English vocabulary learning among students in general basic and secondary education. The research aimed to develop an interactive digital tool that facilitates English vocabulary acquisition, complementing traditional methodologies. The intervention was carried out in a case study with 21 sixth-grade students at the INSUTEC Educational Unit, over a 10-week period. The model was validated through mixed data analysis, including quantitative metrics, such as increased vocabulary scores, and qualitative analyses, such as interviews and observations, to assess student perception and engagement. The results showed that students showed greater interest and engagement, achieving significant increases in their comprehension and use of English vocabulary; furthermore, the model facilitated interaction and promoted independent learning. The conclusions indicate that the interactive website is an effective tool for enhancing English language teaching and can be integrated as a complement to face-to-face classes, addressing different educational needs. A positive aspect was the improvement in motivation and vocabulary acquisition. However, it is recommended that the study be expanded to other contexts to validate its applicability at different educational levels and settings.

A computational data analytics model with Power BI to identify patterns of property crime in Zone 1 of Ecuador

Andrés Roberto León Yacelga — 2025-07-01

This study develops a computational data analytics model applied to property crimes reported in Zone 1 of Ecuador (Esmeraldas, Carchi, Imbabura, and Sucumbíos) during the period 2014–2024. Through the implementation of the Power BI tool, an analytical architecture was designed that enabled the extraction, cleaning, modeling, and interactive visualization of data from ECU911, the Prosecutor's Office, and the National Police. The model employs DAX expressions for the analysis of temporal and spatial relationships, integrating heat maps, time series, and dynamic segmentations. The methodology adopted a mixed approach: quantitative, by processing more than 28,000 complaint records, and qualitative, through interviews with prosecutors and police officers. The results indicate that home burglaries represent 41.6% of all property crimes, with a higher concentration in urban areas of Esmeraldas and Ibarra. Furthermore, only 30% of victims report crimes, revealing a critical gap in security perceptions. The model identified time patterns (highest incidence between 6:00 p.m. and 10:00 p.m.) and critical areas, facilitating the prioritization of operations. The system was validated by comparing predictions of critical areas with data from the first quarter of 2024, achieving an accuracy of 87.4%. It is concluded that tools such as Power BI enhance data-driven decision-making, although they face challenges such as information quality and institutional resilience. It is recommended to strengthen analytical training for officials and promote database interoperability between institutions.

Assessing the impact of deforestation on sedimentation using the CAMF heuristic: Application in Manicaragua, Cuba

Grethell Castillo Reyes — 2025-07-01

This paper applies the CAMF (Cellular Automata-based heuristic for Minimizing Flow) method to identify, from a raster representation, the sites with highest and lowest impact of deforestation on sediment yield from a river catchment in Manicaragua, Cuba. CAMF addresses the spatial interaction in sediment flow by iteratively and incrementally selecting sites based on their marginal contribution to sediment yield, allowing decision-makers to identify critical areas where deforestation should be avoided to protect water resources and zones where deforestation would have minimal impact. The results show that deforesting the first 200 high-impact cells identified by CAMF leads to approximately 30% increase in sediment yield, indicating their high sensitivity for land use change. In contrast, deforestation of up to 50% of cells with the lowest impact results in negligible changes in sediment yield (less than 0.002%). Furthermore, this study outlines a new challenge for future research: using CAMF to identify optimal locations for afforestation as a compensatory measure for unavoidable deforestation, ensuring the continued delivery of ecosystem services with minimal land use.

Hybrid metaheuristic algorithm for cryptographically robust boolean function generation

Lilian Bárbara Pérez Sosa — 2025-07-01

This paper presented the design of a hybrid heuristic algorithm between Simulated Annealing and Tabu Search, capable of finding m-resistant Boolean functions (m=1 or m=2) with high nonlinearity, a high algebraic degree and that satisfy a high order of Propagation Criterion. This algorithm uses as initial candidate solution an algebraically constructed function that possesses the properties of being m-resistant, having high nonlinearity and high algebraic degree; in addition, the algorithm contributes to include the property of a high propagation criterion; thus, directing the search to regions of the space with good properties. The algorithm moves in the search space by preventing the properties of the Boolean function from falling below a pre-specified threshold, while at the same time including properties through the use of a cost function.

Intelligent system for detecting and classifying vulnerabilities in Android applications using data mining and supervised algorithms

Fausto Alberto Viscaino Naranjo — 2025-07-01

An intelligent vulnerability detection and classification system for Android applications was designed and implemented using data mining techniques and supervised algorithms. The research aimed to develop an automated model that identifies vulnerability patterns in native and hybrid Android applications, facilitating specific mitigation strategies. The intervention consisted of analyzing 32 applications (16 native and 16 hybrid) in a case study in an enterprise environment over a period of 14 weeks, using static and dynamic analysis, penetration testing, and security configuration assessments. The model was validated using statistical analyses such as the Chi-square test and performance metrics such as precision, recall, and false positive rate to evaluate its effectiveness in detecting and classifying vulnerabilities. The results indicated significant differences between both types of applications in vulnerability patterns, confirming that the model effectively distinguishes between risks specific to each development. The conclusions show that the system is a valuable tool for security management in enterprise software development, enabling early risk identification and the adoption of precise corrective measures.

Application of nanotechnology for innovation in dental prevention, diagnosis and treatment

Alexis Fernando Chango Quinga — 2025-07-01

This research aims to analyze the application of nanotechnology in prevention, diagnosis, and treatment processes in dentistry, highlighting its clinical benefits and transformative potential. Through a technical review and an educational intervention, the study delved into the use of antibacterial nanomaterials for caries prevention, early diagnosis systems using nanosensors, and treatments such as needle-free anesthesia with nanospheres and advanced implantology with nanostructured surfaces. The intervention was implemented as a case study with 35 higher education students during a 12-week academic cycle, through practical seminars and clinical simulations guided by experts. The impact of learning and content assimilation was assessed using a pre- and post-intervention test, validated with the repeated measures ANOVA statistical test, which showed significant differences in student performance (p < 0.05). The results demonstrated a substantial increase in technical and clinical knowledge about dental nanotechnology. It is concluded that nanotechnology represents a key tool for transforming dental practice in terms of efficacy, precision, and patient comfort. However, greater investment in the ongoing training of oral health care professionals is recommended to ensure the safe and ethical implementation of these emerging technologies.

Computational methodology to assess motivation and knowledge retention

Mónica Gabriela Cháchalo Sandoval — 2025-07-01

A computational methodology was designed to evaluate motivation and knowledge retention in higher education, utilizing advanced statistical analysis techniques and gamification models. The objective was to develop a system that allows quantitative measurement of the impact of gamified strategies on student motivation and memory. The intervention was carried out in a case study with 60 higher education students over a 16-week period, implementing gamified activities with challenges, rewards, and real-time feedback. The computational model was validated using analysis of variance (ANOVA) and correlational analysis, assessing changes in motivation, engagement, and knowledge retention levels before and after the intervention. The results showed a statistically significant increase in motivation and knowledge retention scores, corroborating the effectiveness of the gamified strategies applied. The conclusions indicate that the methodology is an effective tool for enhancing active learning and improving knowledge retention, demonstrating clear benefits for academic performance. However, it is recommended to strengthen teacher training and improve technological capabilities in future implementations to further optimize their results and sustainability. Overall, the study confirms that gamification, when applied appropriately, can transform educational processes into superior environments.

Evaluation of time series models for air traffic flow prediction

Laritza Asán Caballero — 2025-07-01

The continuous growth of air transport has made it essential to accurately predict air traffic flow for effective operational management. This study evaluated and compared five predictive algorithms-linear regression, ARIMA, SARIMA, artificial neural networks, and long short-term memory networks-applied to air traffic time series data. A thorough preprocessing phase was conducted to address seasonality and domain-specific characteristics, followed by a controlled experiment to assess the accuracy and training time of each model. The results showed that neural network-based models achieved the highest prediction accuracy, although at the cost of longer training times. Notably, the artificial neural network model outperformed more complex alternatives, highlighting that the simplest approach can sometimes be the most effective depending on data quality and problem context. This work provides practical guidance for selecting predictive models in the field of air traffic management and emphasizes the importance of adapting solutions to specific scenarios. The findings can contribute to improved planning and decision-making processes by reducing uncertainty in air traffic forecasting.

Supervised statistical classification to analyze the impact of propolis on the bacteria Actinomyces viscosus and Streptococcus mutans

Herman Alfredo Dávila Pinto — 2025-07-01

This study develops a computational model based on supervised statistical classification to analyze the impact of propolis on the bacteria Actinomyces viscosus and Streptococcus mutans, through in vitro tests carried out between November 2023 and April 2024. The research adopted an observational, longitudinal, prospective and analytical design. Data were collected from 60 bacterial strains grown in the laboratory, evaluating sensitivity to propolis through inhibition halos measured with caliper according to the Duraffourd scale (1986). Nonparametric statistical tests (Kruskal-Wallis, Mann-Whitney U) and Chi-square were applied to analyze the quantitative and qualitative effects. The data were modeled using a computational classification system in Python using decision trees, which allowed predicting the level of bacterial sensitivity (no sensitivity, low, high, extremely high) based on the type of bacteria and the inhibitory halo observed. The results revealed that Actinomyces viscosus showed a mean inhibition of 15.60 mm (SD = 1.399), while Streptococcus mutans showed a lower response, with a mean of 0.653 mm (SD = 0.653), indicating low variability. The analysis classified the samples into: 33.3% with no sensitivity, 5% with sensitivity, 48.3% with high sensitivity, and 13.33% with extremely high sensitivity. The model achieved a classification accuracy of 91.2% with 5-fold cross-validation. It is concluded that propolis is more effective against A. viscosus and that the computational model can support clinical decision-making regarding natural antimicrobial therapies.

Computational Mathematics for Downdraft Gasification: Biomass Model, Simulation, and Optimization

Elbis D´Espaux Shelton — 2025-07-01

This paper presents a predictive mathematical model for simulating downdraft biomass gasification. It addresses thermochemical processes and the decomposition of wood residues. The development of differential equations to model heat and mass transfer, and the search for optimization in the energy use of biomass, demonstrate the application of computational mathematics in solving a relevant problem in the field of energy. Acknowledging the limitations of existing models, this paper contributes to the discussion on improving the accuracy and understanding of the complex processes involved in biomass gasification.

Analytical and experimental model to evaluate the behavior of nanoparticles in resin fillings used in dental treatments

Juan Sebastián Michilena Echeverría — 2025-07-01

The research focused on designing an analytical and experimental model to evaluate the behavior of nanoparticles in resin fillings used in dental treatments. The objective was to develop a system that allows analyzing how the incorporation of nanoparticles improves the strength, durability, and biocompatibility of resin fillings. The intervention was carried out in a case study with 50 patients in a university dental clinic over a 6-month period. Fillings with different concentrations of nanoparticles in composite resins were applied, comparing them with traditional fillings. The computational model was validated through analysis of variance (ANOVA) and mechanical strength tests, in addition to evaluating biocompatibility through histopathological analysis in in vitro models. The results demonstrated that nanoparticle fillings showed statistically significant improvements in fracture resistance (p < 0.01) and biocompatibility, compared to traditional fillings. The conclusions indicate that the incorporation of nanoparticles into resin fillings can revolutionize dental treatments, offering greater advantages in durability and safety. Although the results are promising, further clinical studies with larger samples and different concentrations are recommended to optimize their application. This study confirms the potential of nanoparticles to improve the quality of dental restorations and their longevity in patients.

Using DTW as a distance measure from smartphone sensor data

Arlety Leticia García García — 2025-06-03

In recent years, the use of smartphones to capture data from built-in sensors has been of interest to the scientific community. Its widespread use makes it easy to obtain this type of data in a discreet and economical manner. This article presents the use of DTW (Dynamic Time Warping) as a distance measurement for data obtained during various activities while carrying a smartphone. To this end, an Android application was developed to capture the data. The data obtained from the phone's accelerometer and gyroscope sensors are processed to obtain a time series x,y,z. The interest in this research lies in the possibility of using it in the classification of patients with Parkinson's disease in everyday activities.

Perceptions of gender approach in Computer Engineering Students

Paula Yuleni Hechavarría Leyva — 2025-07-01

The gender gap in computer engineering remains a global challenge, and Cuba is no exception. At the University of Informatics Sciences (UCI) in Havana, differences in students' perceptions of the gender approach persist, which may affect equity in access and professional development in the technology sector. This research analyzes perceptions of gender equity in computer engineering through a survey conducted among 400 UCI students during the period from 2014 to 2019. The study aimed to identify barriers and opportunities for the inclusion of women in this field, exploring factors such as self-efficacy, female representation in academic and professional settings, and gender stereotypes. The methodology included a structured questionnaire with questions about academic experiences, professional expectations, and perceptions of the educational environment in terms of equity. Findings revealed that although women achieve high academic performance, many perceive obstacles in their professional development due to a lack of female role models and gender biases in the workplace. Furthermore, it was found that men tend to underestimate the existence of structural barriers. The central conclusion highlights the need for institutional strategies to promote gender equity and raise awareness among the student community about the importance of a more inclusive environment. This study underscores the relevance of implementing educational policies and mentorship programs that encourage female participation in computer engineering.

5E strategy with digital technologies for creative, reflective thinking in natural sciences: neutrosophic validation

Luis Fernando Lainez Vera — 2025-07-01

This study investigates how to promote creativity and metacognitive thinking in a seventh-grade science classroom using the 5E model and digital technologies. The central question is how to stimulate these skills in this context, relevant given the scarcity of innovation and critical thinking in today's world. The research seeks to overcome rote learning and strengthen understanding of the relationship between knowledge and real-life problem-solving. The literature review indicates that the 5E model fosters active participation, even in unmotivated students, especially when combined with digital tools such as simulations and data analysis, although a systematic implementation linking creativity and metacognition is lacking. The methodology includes practical activities, interdisciplinary connections with art, and digital portfolios, assessed using a multi-criteria approach that considers uncertainty in the educational process. Previous studies show that the use of digital technologies in the 5E method fosters interest, exploration, critical thinking, and problem-solving. The project proposes innovative teaching that transcends the New York State curriculum and offers a model that can be transferred to other educational settings, providing teachers with tools to make pedagogical adjustments that generate more practical and effective learning today.

Digital tools for legal oversight of visitation rights and the protection of the child's best interests in the Ecuadorian context

Martha Asunción Franco Monserrat — 2025-07-09

This research analyzed the legal and technological risks involved in the digital implementation of visitation rights in Ecuador, with an emphasis on shared parental responsibility and the protection of the child's best interests. A qualitative methodology based on structured interviews with experts, doctrinal and comparative analysis, and the design of technological solutions was used. As a result, a reform to the Code of Children and Adolescents was proposed, including shared custody and the sanction of parental alienation. Furthermore, a digital legal instrument was designed for the supervision of visitation rights, which was validated by experts using the trial method, obtaining positive ratings for feasibility, relevance, usability, and impact on the child. The findings show that the use of technologies can strengthen family justice, provided that ethical principles such as privacy and respect for family life are safeguarded. The research concludes that the articulation between law, technology, and ethics is essential to guarantee more effective, equitable, and child-oriented judicial processes.

Web system for scheduling dental appointments online with the inclusion of a ChatBot

Mylena Michelle Lozada Suárez — 2025-07-09

The Xperts Dental Clinic, located in La Maná, Ecuador, was facing difficulties with its appointment scheduling process, which was managed manually via WhatsApp. This caused disorganization and reduced service efficiency. To address these limitations, a web-based system for online dental appointment scheduling was developed, incorporating an intelligent chatbot using Natural Language Processing (NLP) technology. This system, based on Django, MySQL, and Rasa on Python, allows patients to self-manage their appointments, automate responses to frequently asked questions, and dynamically coordinate appointment schedules, promoting a more accessible and modern experience. The architecture employed was a client-server system, with a user-friendly and responsive interface built with HTML5, CSS3, and JavaScript, and scalable integration through RESTful APIs. The SCRUM methodology facilitated modular development, allowing functionalities to be validated and adjusted in short cycles, while simulation and machine learning techniques optimized appointment allocation and rescheduling in the face of variable patient arrivals. The system validation considered both technical performance and user satisfaction, including patients and administrative staff. The results showed positive acceptance, especially in terms of speed, convenience, and effectiveness of reminders. The chatbot also demonstrated favorable results, although with areas for improvement. The implementation significantly reduced no-shows, from 27% to 12%, attributed to 24/7 accessibility and the automation of appointment reminders and modifications. Furthermore, the wait time for dental care was cut in half, from 18 to 9 minutes on average, optimizing appointment scheduling and reducing administrative bottlenecks.

Impact of the lack of technological interoperability on effective judicial protection and procedural speed in Ecuador

Jadira Alexandra Guamani Defaz — 2025-07-11

This research examined the impact of the lack of technological interoperability between public institutions on effective judicial protection and procedural expediency in Ecuador. Using a mixed-methods approach, the study involved a comprehensive analysis of the national and international regulatory framework, surveys of justice users and practitioners, interviews with experts, and a validation process for a legal and technical proposal aimed at overcoming the identified barriers. The results showed that the Ecuadorian system lacks robust regulations requiring information sharing between public entities, which generates delays, duplication of processes, and affects the right of access to timely justice. Ninety-one percent of respondents acknowledged that the lack of interoperability unnecessarily prolongs judicial proceedings, while experts agreed that it violates essential principles of due process. A regulatory and technical proposal was designed that includes reforms to current procedures and the strengthening of interoperability standards, obtaining an average validation score of 4.7 out of 5 for relevance, feasibility, and impact. The findings suggest that moving toward an interoperable system among public institutions is essential to ensuring efficiency, transparency, and the effective protection of rights in the Ecuadorian judicial system.

System of normative references to regulate judicial motivation through artificial intelligence and human control

Jefferson Daniel Verdezoto Sumba — 2025-07-11

This research proposes a system of normative references to regulate the use of artificial intelligence (AI) as an auxiliary tool in judicial motivation in Ecuador. The proposal was structured around five fundamental axes: constitutional and procedural principles, current regulations, comparative experiences, doctrinal and ethical foundations, and technical considerations. The study was carried out in three phases. In the first, a documentary analysis of national and international normative and doctrinal sources was conducted. In the second, the system of references was designed, broken down into technical and legal subcriteria. In the third phase, the proposal was validated through expert judgment, applying a Likert scale based on criteria of relevance, conceptual clarity, operational feasibility, and legal relevance. The results confirmed the normative coherence, technical applicability, and ethical compatibility of the model. In addition, a logical architecture of the computational system was developed, with modules for normative analysis, ethical reasoning, generation of suggested motivation, and judicial review. The conclusion is that AI can be legitimately integrated into the judicial process, provided that regulatory controls and guarantees of transparency, explainability, and human review are established. This proposal represents a step toward more efficient, yet legally sound, digital justice.

Clinical simulation models in health sciences teaching: a systematic review

Mónica Gabriela Cháchalo Sandoval — 2025-07-01

A comprehensive systematic review of clinical simulation models in health sciences education was conducted, using the PRISMA methodology to identify and assess the available evidence. A total of 62 studies published between 2010 and 2023 were analyzed in academic databases such as PubMed, Scopus, and Web of Science. The initial search generated 175 articles, of which 22 met the inclusion criteria after excluding duplicates, irrelevant studies, and those with insufficient methodological quality (assessed using the PRISMA checklist). The review focused on analyzing variables such as improvements in knowledge retention (presented in 11 studies), development of practical skills (7 studies), clinical decision-making (2 studies), increased confidence, and error reduction (2). The findings demonstrated that clinical simulation is significantly more effective than traditional methods in these aspects (p < 0.05 in most studies), although it was noted that its implementation presents challenges related to cost, training, and resistance to change in the educational field. Consolidated evidence indicates that simulation is a powerful complementary strategy, recommending its hybrid integration with conventional teaching. It is suggested that institutional policies be promoted to facilitate its adoption and the development of specialized skills to maximize its impact on health sciences training.

Application of artificial intelligence and machine learning models and tools in oral surgery using laser technology: a bibliographic review

Kahita Paola Paca Zhingre — 2025-07-01

A systematic literature review was conducted on the application of artificial intelligence and machine learning models and tools in oral surgery, aiming to identify trends, benefits, limitations, and challenges in their clinical integration. The research used the PRISMA methodology, evaluating a total of 15 articles published between 2015 and 2025, selected after a search in databases such as PubMed, Scopus, and Web of Science. The studies analyzed employed machine learning techniques, neural networks, classification algorithms, and image processing, focusing on diagnosis, surgical planning, risk prediction, and treatment personalization. The findings showed that these models improve diagnostic accuracy, optimize decision-making, and reduce clinical errors, although they face challenges related to data quality, model interpretability, and implementation costs. The results indicated that most studies (p < 0.05) demonstrated statistically significant benefits in improving clinical outcomes and the efficiency of surgical procedures, highlighting the potential of AI technologies to transform dental practice. However, barriers to widespread adoption remain, such as the need for protocol standardization, staff adaptation, and consideration of ethical issues. The results suggest that the use of artificial intelligence tools in oral surgery represents an innovation with high potential, although greater efforts are required to overcome obstacles and promote its effective integration into daily clinical practice.

Methodological Contributions to teaching practice: Systematic Review

Yessica Vanesa Arteaga Ponce — 2025-07-22

This article presents a systematic review of recent scientific literature with the aim of determining methodological contributions in teaching practice. Fifty studies published since 2023 were examined, selected through recognized databases (Scopus, Scielo, Web of Science, and Google Scholar), following the guidelines of the PRISMA 2020 protocol. The results show a methodological evolution grounded in constructivist, collaborative, and inclusive approaches, highlighting the incorporation of emerging technologies, formative assessment, and didactic flexibility as central axes of innovation. There is evidence of the need to rethink teaching praxis from a holistic perspective that articulates pedagogical, relational, and institutional aspects, considering personal, dynamic, and value dimensions. The review underscores the key role of professional reflection, continuous training, and inclusive leadership as essential elements for an effective, contextualized, and sustainable pedagogical transformation.

Use of mobile applications among early childhood education students in Ecuador, 2025

María Miryan Macias Alvarado — 2025-07-22

The purpose of this study was to analyze the use of mobile applications among early childhood education students in Ecuador in the year 2025. A systematic review of the scientific literature was conducted using the PRISMA system. A total of 500 articles were initially retrieved and analyzed, ultimately selecting a total of 50. The analysis and systematization of the 50 articles included revealed that most mobile applications used in early childhood education in Ecuador exhibit a high degree of usability and accessibility, with 33 articles (66%) reporting these aspects as positive. This demonstrates that most applications are easy to use and effectively designed to interact with students. However, 12 articles (24%) reported an intermediate level of usability, indicating that there are areas that need improvement, such as simplifying interfaces and improving accessibility. A small percentage, five articles (10%), reported problems with lower-level applications, highlighting the importance of examining and modifying less useful applications to ensure their effectiveness for all users.