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All Journal Bulletin of Electrical Engineering and Informatics Emerging Science Journal
Criollo-C, Santiago
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Electrical & Electronics Engineering Environmental Science

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Using Mixed Reality (MR) as an Emerging Technology for Improving Higher Education: Analysis of Mental Workload Criollo-C, Santiago; Guerrero-Arias, Andrea; Buenaño-Fernández, Diego; Jaramillo-Alcazar, Ángel; Luján-Mora, Sergio
Emerging Science Journal Vol 8 (2024): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-SIED1-024

Abstract

This study aims to evaluate the mental workload perceived by students when using Build_3D, a mixed reality (MR) application, as an educational tool for learning PC and smartphone hardware, as well as to analyze teachers' perceptions of its impact on the teaching process. The NASA-TLX tool was applied to measure mental workload in 60 students, assessing six dimensions: mental demand, physical demand, temporal demand, perceived performance, effort, and frustration level. Additionally, qualitative observations were collected from teachers regarding the use of MR in practical learning environments. The results show that the perceived performance dimension achieved the highest score, highlighting the application’s effectiveness in improving learning outcomes. Mental and temporal demands were moderate, while effort, frustration, and physical demand were low. Teachers noted that Build_3D enhances practical learning by enabling the repetition of complex tasks and fostering student motivation through immersive experiences. As a novel contribution, the study highlights the capacity of MR tools to integrate theoretical and practical concepts in an interactive environment, reducing cognitive load and promoting autonomous and personalized learning. Doi: 10.28991/ESJ-2024-SIED1-024 Full Text: PDF
Enhancing cloud resource management: leveraging adversarial reinforcement learning for resilient optimization Dwinggo Samala, Agariadne; Rawas, Soha; Criollo-C, Santiago
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10636

Abstract

This paper introduces the first adversarial reinforcement learning (ARL) framework for resilient cloud resource optimization under dynamic and adversarial conditions. While traditional reinforcement learning (RL) methods improve adaptability, they fail when faced with sudden workload surges, security threats, or system failures. To address this, we propose an ARL-based approach that trains RL agents using simulated adversarial perturbations, such as workload spikes and resource drops, enabling them to develop robust allocation policies. The framework is evaluated using synthetic and real-world Google Cluster traces within an OpenAI Gym-based simulator. Results show that the ARL model achieves 82% resource utilization and a 180 ms response time under adversarial scenarios, outperforming static policies and conventional RL by up to 12% in terms of cost-effectiveness. Statistical validation (p0.05) confirms significant improvements in resilience. This work demonstrates the potential of ARL for self-healing cloud schedulers in production environments.
Co-Authors Agariadne Dwinggo Samala Buenaño-Fernández, Diego Guerrero-Arias, Andrea Jaramillo-Alcazar, Ángel Luján-Mora, Sergio Rawas, Soha