Barrera Ánimas, Ari Yair

Preferred name

Official Name

Barrera Ánimas, Ari Yair

Alternative Name

aribarrera

Barrera-Animas, Ari Y.

Main Affiliation

ORCID

0000-0002-8533-125X

Scopus Author ID

57191490159

Researcher ID

D-5296-2016

Now showing 1 - 4 of 4

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Benchmark of Wrist-Wearable Devices for Student Stress Monitoring
(Springer Nature Switzerland, 2025)
Mena-Martinez, Alma
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Alvarado-Uribe, Joanna
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Garcia-Ceja, Enrique
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Escamilla-Ambrosio, Ponciano Jorge
This study presents a comparative analysis of various activity wristbands to evaluate their suitability as a tool for collecting human activity among university students. The research examines key factors such as battery life, data extraction methods, file formats, integrated sensors, and developer support. The results reveal significant differences in device capabilities, with the Garmin Venu 3 offering the most comprehensive sensor suite but at a high cost, while the Fitbit Inspire 3 provides a cost-effective alternative with essential monitoring features. The study highlights the importance of compatibility with data processing tools and the ability to extract information efficiently for research applications. These insights contribute to the selection of suitable wearable devices for academic studies. ©The authors ©Springer.
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Designing a gamified approach for digital design education aligned with Education 4.0
(Frontiers Media SA, 2024)
Cal y Mayor-Peña, Francisco
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Escobar-Castillejos, Daisy
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Noguez, Julieta
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Introduction: Gamification integrates game-like elements, such as points, badges, and leaderboards, into the educational process. This study examines the influence of a gamified approach on improving graphic design education. By implementing this methodology, we aim to create a more dynamic learning environment that could lead to better academic outcomes. Methods: A quasi-experimental design was employed to compare students' average grades and academic achievements using the gamified technique with those taught using conventional methods. Thirty-two students participated in the study, with these students enrolled in three different terms. Data collection involved tracking students' grades, participation, and completion rates of gamified activities. Results: Participants in the August—December 2023 semester (Experimental 2 group) who experienced the gamified approach with the proposed platform showed significant improvement, with a p-value of 0.033, compared to those in the August—December 2022 semester (Control group), which used only conventional approaches. Furthermore, better learning outcomes were obtained when the Experimental 2 group was compared with the January-May 2023 semester (Experimental 1 group), which used only the gamification methodology (p-value = 0.025). Additionally, out of 15 students in the Experimental 2 group, 10 achieved certification in Adobe Illustrator and 13 in Photoshop, suggesting that gamification elements applied through a digital platform can improve academic performance and enhance students' practical skills and readiness for professional challenges in graphic design. Discussion: Results indicate that the gamified methodology can improve learning outcomes. Nevertheless, the proposed approach also has limitations and areas for improvement. Manual data capture, integration with external tools, the amount of teachers applying the approach, and the sample size of participants are limitations of the study that could have affected the accuracy of the results. Future work will focus on developing a proprietary platform that integrates course content and automates the tracking system to improve efficiency and accuracy. Moreover, a subsequent study will include a larger sample of students and professors to validate the present study's findings. ©The authors ©Frontiers Media SA ©Frontiers in Education.
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Enhancing STEAM in Education 4.0: A Review of Data-Driven Technological Improvements
(Springer, 2024-01-01)
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Noguez, Julieta
Universities have fulfilled the role of organizations dedicated to the promotion of intellectual knowledge and the fostering of creative thinking. Educational institutions function as the central place for students to acquire specialized knowledge in their respective disciplines, while also developing competencies and beliefs that contribute to the progress of society. As technology advances, universities are facing a new revolution in the paradigms of education, which is making them adapt their established methodologies. Education 4.0 aims to improve education through cutting-edge technology and automation. Furthermore, the use of data-driven methodologies allows educators to automate the processes of assessment and evaluation, which could enhance the accuracy of comprehending educational outcomes. Universities are currently promoting the integration of analytical reasoning and creative expression using multidisciplinary techniques such as STEAM. This approach ensures that students not only acquire technical expertise, but also develop the essential skills of creativity, adaptability, and humanistic understanding that are necessary in the current job market. The objective of this chapter is to discuss the development and importance of STEAM within Education 4.0, emphasizing the potential of data-driven technologies. ©Springer
Scopus© Citations 1 47
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Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review
(JMIR Publications Inc., 2025-11-18)
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Julieta Noguez
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Alejandra J Magana
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Bedrich Benes
<jats:sec> <jats:title>Background</jats:title> <jats:p>Artificial intelligence (AI) has introduced novel opportunities for assessment and evaluation in surgical training, offering potential improvements that could surpass traditional educational methods.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This scoping review examines the integration of AI in surgical training, assessment, and evaluation, aiming to determine how AI technologies can enhance trainees’ learning paths and performance by incorporating data-driven insights and predictive analytics. In addition, this review examines the current state and applications of AI algorithms in this field, identifying potential areas for future research.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, the PubMed, Scopus, and Web of Science were searched for studies published between January 2020 and March 18, 2024. Eligibility criteria included English-language full-text articles that investigated the application of AI in surgical training, assessment, or evaluation; non-English texts, reviews, preprints, and studies not addressing AI in surgical education were excluded. After duplicate removal and screening, 56 studies were included in the analysis. Data were structured by categorizing studies according to surgical procedure, AI technique, and training setup. Results were synthesized narratively and summarized in frequency tables.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>From 1400 initial records, 56 studies met the inclusion criteria. Most were journal articles (84%, 47/56), with the remainder being conference papers (16%, 9/56). AI was most frequently applied in minimally invasive surgery (27%, 15/56), neurosurgery (20%, 11/56), and laparoscopy (16%, 9/56). Common techniques included machine learning (20%, 11/56), clustering (14%, 8/56), deep learning (11%, 6/56), convolutional neural networks (11%, 6/56), and support vector machines (11%, 6/56). Training setups were dominated by simulation platforms (33%, 19/56) and box trainers (24%, 13/56), followed by surgical video analysis (16%, 9/56), and robotic systems such as the da Vinci platform (13%, 7/56). Across studies, AI-enhanced training environments provided automated skill assessment, personalized feedback, and adaptive learning trajectories, with several reporting improvements in trainees’ learning curves and technical proficiency. However, heterogeneity in study design and outcome measures limited comparability, and algorithmic transparency was often lacking.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The application of AI in surgical training demonstrates the potential to enhance skill acquisition and support more efficient, personalized, and adaptive learning pathways. Despite encouraging findings, several limitations exist, including small sample sizes, the lack of standardized evaluation metrics, and insufficient external validation of AI models. Future studies should aim to clarify AI methodologies, improve reproducibility, and develop scalable, simulation-based solutions aligned with global education goals.</jats:p> </jats:sec>

Barrera Ánimas, Ari Yair

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