CRIS

Permanent URI for this communityhttps://cris-udd.scimago.es/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 132

Some of the metrics are blocked by your
consent settings
CONGA: CONscientization GAme for Colon Cancer Literacy in Last-Semester Software Engineering Students
(2026)
Franklin Parrales-Bravo
;
Jonatan Guillen-Salabarria
;
Jadán Guerrero, Janio
;
Leonel Vasquez-Cevallos
This study aimed to evaluate the effectiveness of the CONGA game, an interactive and gamified digital tool that uses AI-generated or manually created questions with feedback, to improve colon cancer literacy among tenth- semester Software Engineering students at the University of Guayaquil. Grounded in Paulo Freire’s critical pedagogy, CONGA operationalizes the concept of “conscientização” (critical consciousness awakening) by engaging learners in dialogical reflection on medical myths and encouraging critical evaluation of health information sources. This work addresses an age group—emerging adulthood—that is often overlooked in cancer prevention campaigns despite increasing cancer incidence in this population. The game incorporates an adaptive engine that personalizes difficulty and scoring based on player performance, enhancing engagement and learning personalization. A controlled experiment compared the game-based intervention with traditional lecture-based instruction, using pre- and post-test assessments to measure knowledge gains and misconception reduction. Results demonstrated that the CONGA group achieved a significantly higher post-test correct response rate of 82%, compared to 57% in the traditional instruction group, and showed a 70.4% reduction in incorrect responses versus 42.4% in the control group. These findings indicate that CONGA’s adaptive, feedback-driven design was more effective in enhancing short-term knowledge acquisition and immediate conceptual clarification following a single session. The study concludes that, based on immediate post-intervention assessments, gamified learning represents a scalable and engaging pedagogical strategy for colon cancer literacy, particularly in our local younger population. However, these results reflect short-term learning gains measured immediately after a single session, and further research is needed to evaluate long-term knowledge acquisition.
1
Some of the metrics are blocked by your
consent settings
Artificial Intelligence for the Diagnosis of Respiratory Diseases in Dogs and Cats: A Systematic Review
(2026)
Franklin Parrales-Bravo
;
Jadán Guerrero, Janio
;
Katherine Medina-Castro
;
Rosangela Caicedo-Quiroz
Respiratory diseases represent a leading cause of veterinary consultations in dogs and cats, yet their detection remains challenging due to clinical variability and subjective interpretation of traditional diagnostic methods. In recent years, artificial intelligence (AI) has emerged as a promising tool to augment veterinary diagnostics through automated analysis of imaging and physiological data. This systematic review synthesizes and critically evaluates 24 studies published from 2019 onward that explore AI applications to support the detection of respiratory diseases in dogs and cats, focusing on three complementary modalities: audio-based (e.g., respiratory sounds), image-based (e.g., chest radiographs), and multimodal approaches. Our findings indicate that deep learning models, particularly convolutional neural networks (CNNs) and transformer architectures, achieve clinically relevant accuracy in detecting conditions such as cardiomegaly, alveolar patterns, and Brachycephalic Obstructive Airway Syndrome (BOAS). However, significant barriers remain, including data scarcity, lack of standardized datasets, and limited real-world validation. This review highlights the transformative potential of AI in veterinary respiratory diagnostics while underscoring the need for collaborative efforts in data sharing, methodological standardization, and clinical integration to realize its full impact in practice.
3
Some of the metrics are blocked by your
consent settings
Comprehensive Plan for the Induction of Environmental Awareness Based on Household Waste
(2026)
Jadán Guerrero, Janio
This article presents the results of an investigation applied to 30 residents of a neighborhood in the municipality of Piedecuesta, Santander. The purpose of this research is to develop a comprehensive plan to promote environmental awareness based on household waste (DR), through the application of a survey that allows to know the previous knowledge of the community about the environmental management generated daily in their homes. The strategy employed focuses on environmental education talks, interactive presentations and the delivery of a brochure that comprehensively addresses environmental awareness in relation to household waste. A mixed methodology was used in the research, through the following phases: exploration, planning, observation and reflection. Finally, the following findings were obtained: it is observed that the community lacks information on the separation of DR: organic, recyclable and non-recyclable. In conclusion, the lack of environmental education in the municipality has resulted in a large amount of household waste that is neither separated nor properly used, which generates several negative consequences such as: environmental pollution, greater accumulation in landfills, greenhouse gases (GHG), among others. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
5
Some of the metrics are blocked by your
consent settings
Risk-Aware Fleet Management in Public Enterprises: A Machine Learning Approach Using Web-Scraped Data
(2025)
Tania Calle-Jimenez
;
Flavio Ibujes-Calle
;
Jadán Guerrero, Janio
;
Sandra Sanchez-Gordon
In recent years, technological advancements, particularly in artificial intelligence and machine learning, have enabled the automation of tasks once thought impractical. However, many public sector organizations continue to rely on manual processes, especially in areas like vehicle fleet management, where critical operational data remains underutilized. This study addresses that gap by proposing a machine learning model aimed at improving vehicle fleet management in public enterprises. The model focuses on classifying drivers based on their risk levels, leveraging behavioral data, individual driver characteristics, and patterns of vehicle usage to provide actionable recommendations for fleet optimization. A key innovation of this work is the integration of web scraping techniques to automatically collect and update data related to drivers, vehicles, and fleet operations. This significantly reduces the dependency on manual data entry and supports the automation of processes such as vehicle registration validity control. The proposed system also includes the development of driver risk classification models, with results visualized through an interactive dashboard and geospatial map to facilitate strategic decision-making. This approach enhances the efficiency, transparency, and data-driven decision-making capabilities of public entities managing transportation assets. © 2025 IEEE.
9
Some of the metrics are blocked by your
consent settings
Web Accessibility in the Portals of the Countries in the Latin American Index of Artificial Intelligence
(2025)
Patricia Acosta-Vargas
;
Gloria Acosta-Vargas
;
Belén Salvador-Acosta
;
Jadán Guerrero, Janio
This study assesses the web accessibility of portals from the Latin American Artificial Intelligence Index (ILIA) countries, emphasizing the digital inclusion of users with disabilities. Using the Web Content Accessibility Guidelines (WCAG 2.2) as a reference point, the study focuses on the four principles of accessibility: perceptibility, operability, comprehensibility, and robustness. The results show that 43.20 % of the sites met the (minimum) contrast requirements, and 26.48 % met the navigability W3C recommendations. Chile (ranked first, score 73.07) presented 15 contrast issues and six errors overall, demonstrating a firm adherence to accessibility. Brazil (ranked second, with a score of 69.30) showed six contrast issues and eight errors, indicating a solid performance. However, Cuba and Venezuela had significant problems, with 25 and 34 errors, respectively. In contrast, Uruguay ranked high with no errors in contrast and perceptibility, highlighting its leadership in web accessibility in the region. These findings provide valuable insights for policymakers and developers looking to improve accessibility and digital inclusion across Latin America's digital infrastructure.
3
Some of the metrics are blocked by your
consent settings
Gamified Environments as a Pedagogical Strategy in University Education
(2025)
Marcos Chacón-Castro
;
Juan Murillo-Morera
;
Jadán Guerrero, Janio
The technological boom, the updating and the use of digital tools has caused education to undergo changes to face new educational needs, granting and giving prominence to virtuality as a means of autonomous, disciplined and efficient learning. In that order of ideas, this research aims to implement an Escape Room as a strategy to strengthen digital skills in undergraduate students. The online game, designed in the form of a virtual ‘Escape Room’ with various rooms, poses a series of associated challenges to young people for the development and validation of digital skills necessary for the training process they will face. The strategy is based on the action research methodology: planning, observation, action and reflection. The Escape Room integrates the dynamics, elements and mechanics of the game, with the purpose of increasing motivation in learning digital tools in University Education. In the process of evaluating the intervention, an intervention will be applied to 800 students from first to third cycle of a private university. The findings obtained have shown an impact on motivational change through a proactive attitude in learning, the use of digital tools and the creation of virtual content.
12
Some of the metrics are blocked by your
consent settings
Exploring Validity and Reliability in Cognitive Skills Assessment: A Comprehensive Dataset on Critical Thinking, Creativity, and Problem-Solving
(2025)
Ximena Cumanda Miranda Lopez
;
T. Susana A Arias
;
Jose Luis Aguirre
;
Jadán Guerrero, Janio
Economic models are essential in guiding national development. This research introduces a fundamental regression model that utilizes clustering methods to systematically group economic categories based on differences in investment levels and time frames. The findings deepen the comprehension of minimal investments and their associated timelines, offering insights that may assist in the economic modeling relevant to the equatorial region
15
Some of the metrics are blocked by your
consent settings
Generative AI in Higher Education: Bridging Research, Ethics, and Curriculum Innovation
(2025)
Susana A. Arias
;
Robert Vaca Alban
;
Luis Arias Villaroel
;
Jadán Guerrero, Janio
In this paper we explore integrating generative AI methods into educational frameworks, and its potential to increase accessibility, the point of view, to foster} critical thinking, and to force re-evaluation by both educator and learner educator and learner alike. An extensive review of the research trends, key players and thematic areas suggest that machine learning, data science, and applied computing are the key drivers behind generative AI in education. To help promote responsible use, we discuss some ethical questions like fairness, transparency, and bias reduction. By matching technological innovations with educational goals, this study provides tangible suggestions for curriculum design, faculty development, strategic alliance development, assisting educational practice to be transformed by AI in the current academic environment.
7
Some of the metrics are blocked by your
consent settings
The Rhythmic and Emotional Landscape of Beethoven’s Symphony No. 5: A Multi-phase Analytical and Critical Thinking Approach
(2025)
Ximena Cumanda Miranda López
;
T. Susana A. Arias
;
Luis Arias Villaroel
;
Jadán Guerrero, Janio
This study addresses the complex rhythmic and emotional aspects of Beethoven’s Symphony No. 5 using a three-phase analysis. The first pass gives a score to the emotional intensity of each motion, measuring descriptors like “Tension,” “Serenity” and “Triumph” to capture the dynamic alterations of the symphony. During Semantic Analysis IO phase, we Find semi announcement rhythmic patterns and turns to demarcate frequency—based identifiable sequences. The last stage contextualizes these findings by comparing musical patterns with natural phenomena including biological rhythms and environmental cycles. These phases combine to offer a rich and varied approach to the interaction between musical form and its emotional and natural associations, as well as tying into one of the more comprehensive dis-course on the relationship between music, emotion and the natural environment
16
Some of the metrics are blocked by your
consent settings
Bioeconomic Strategies for Sustainable Efficiency in Rice Production: An Adaptive Approach to Optimizing Yield, Cost, and Environmental Impact
(2025)
Susana A. Arias T.
;
Victor H. Dominguez
;
Tania Carolina Tapia
;
Jadán Guerrero, Janio
The challenge for rice production today is how to improve yields without overly increasing pressure on natural resources. Modern farm practices often orient toward short-term returns, leading to runoff, soil erosion, greenhouse gas emissions, and other negative consequences. It underscores the urgent need for measures that balance productivity against environmental stewardship. This research explores a flexible model aimed at enhancing yield efficiency in rice cultivation while reducing the ecological impact. The first and foremost issue addressed is that of input dependency, nitrogen fertilizers in particular, which may result in economic loss as well as environmental consequence. We want the input to be optimized and the system to be sustainable for production. An adaptive efficiency model was applied to simulate different combined reductions and changes of inputs from heterogeneous rice-growing regions. The system also provided real-time feedback on data allowing for dynamic adjustments to inputs such as fertilizers and irrigation, making recommendations based on actual performance. We also included environmental metrics like CO2 emissions to evaluate potential sustainability benefits. The study showed that modest reductions in fertilizer application substantially increased N use efficiency, reducing emissions by up to 20%, without compromising yield. Productivity gains were further modified by cost adjustments across domains without straining any resource. These results suggest that a responsive, feedback-driven approach can reconcile high-yield production with stewardship. The model addresses this issue by optimizing input utilization, making it a potential solution for resilient, eco-friendly rice cultivation
14

CRIS

Browse

Filters

Settings

Sort By

Results per page

Search Results