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Extended reality for education: Mapping current trends, challenges, and applications Samala, Agariadne Dwinggo; Bojic, Ljubisa; Rawas, Soha; Howard, Natalie-Jane; Arif, Yunifa Miftachul; Tsoy, Dana; Coelho, Diogo Pereira
Jurnal Pendidikan Teknologi Kejuruan Vol 7 No 3 (2024): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v7i3.37623

The advancements in 5G technology and Artificial Intelligence (AI) have accelerated the integration of immersive technologies such as Extended Reality (XR) into educational practices. There is a notable scarcity of studies focusing specifically on the applications and impact of XR in academic settings. Most existing research has concentrated on AR and VR, leaving a gap in understanding the full potential of XR. Addressing these gaps and challenges is crucial for harnessing the full potential of XR in education. This study aims to map and analyze the applications, trends, and educational challenges of XR technology. This study conducts a bibliometric analysis covering XR's application in education from 2018 to 2023, analyzing 32 articles from Scopus sources. Key findings highlight XR's annual growth in research publications, with significant contributions from the United States, China, and Canada. XR enriches education by facilitating immersive simulations, real time interaction with virtual objects, and spatial manipulation in three dimensions. It fosters presence and embodiment in virtual environments, supports practical training through realistic simulations, enhances multi-sensory engagement, promotes collaborative learning environments, and improves accessibility for diverse learners. The main challenges of XR technology include high costs, technical hurdles, regulatory issues, infrastructure limitations, and the need for digital literacy and skills. Addressing these challenges, collaborative efforts among educators, researchers, and industry stakeholders are required. Such collaboration is crucial for harnessing the full potential of XR technology to revolutionize education and prepare learners for a dynamic future.

Development of Interactive Flipbook-Based E-Module for Teaching Algorithms and Basic Programming in Higher Education Mahendri, Rama Putra; Amanda, Mita; Latifah, Ulfi; Rawas, Soha
Journal of Hypermedia & Technology-Enhanced Learning Vol. 1 No. 1 (2023): Journal of Hypermedia & Technology-Enhanced Learning—Digital Frontiers
Publisher : Sagamedia Teknologi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58536/j-hytel.v1i1.18

The COVID-19 pandemic has forced educational institutions to switch to online learning, which has posed challenges related to the lack of interactive digital teaching materials. Specifically, the available materials in the Algorithm and Basic Programming course are static and text-based, making it difficult for students to study independently. This study aims to develop an interactive digital book-based e-module with multimedia features to enhance student engagement and understanding and evaluate the quality and effectiveness of the e-module. The study used the 4D (Define, Design, Development, Disseminate) development model. The Define stage involved a needs analysis, while the Design stage included media selection, video preparation, and quiz development. The Development stage consisted of validation by three expert validators and limited trials with 30 students. Data analysis used descriptive methods by calculating the validation percentage and trial results. The validation results showed an eligibility of 89.00% for media and 88.33% for content, both in the "excellent" category. The limited trial received positive responses, averaging 81.00%, indicating that the e-module is easy to use and can increase learning motivation. This e-module is highly suitable for online learning in the Algorithm and Basic Programming course. Further research is recommended to test the effectiveness of this e-module on a larger scale and evaluate its long-term impact on student learning outcomes.

Bias in artificial intelligence: smart solutions for detection, mitigation, and ethical strategies in real-world applications Samala, Agariadne Dwinggo; Rawas, Soha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp32-43

Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.

ChatGPT: a bibliometric analysis and visualization of emerging educational trends, challenges, and applications Samala, Agariadne Dwinggo; Sokolova, Elizaveta Vitalievna; Grassini, Simone; Rawas, Soha
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i4.28119

This study conducts a comprehensive bibliometric analysis and visual exploration of the chat generative pre-trained transformer (ChatGPT) literature in 2023, focusing on its trends, challenges, and applications in education. Using RStudio for bibliometric analysis and VOS viewer for data visualization, this study examines publications from the Scopus database. Following the preferred reporting items for systematic reviews and metaanalyses (PRISMA) guidelines, the systematic review process reinforces the robustness of the analysis. The finding reveals notable trends in the utilization of ChatGPT. Key insights underscore ChatGPT’s increasing role in enhancing engagement, facilitating personalized learning, and fostering student creativity and critical thinking. However, its integration into education encounters obstacles, including ethical considerations, issues of academic honesty, and the imperative for precise usage guidelines; notable applications of ChatGPT encompass language learning, tutoring, automated feedback provision, and functioning as a virtual assistant. These applications showcase ChatGPT’s potential to reshape the educational landscape by introducing innovative pedagogical methods and enriching the student experience. This combined bibliometric and visual analysis provides a comprehensive view of the current status of ChatGPT within the educational domain. It provides a snapshot of the role of ChatGPT in education, offering valuable insights for future research endeavors.

The impact of project-based learning on 21st century skill development of vocational engineering students: A systematic literature review Rozan, Alief Depa; Syahri, Budi; Prasetya, Febri; Fortuna, Aprilla; Samala, Agariadne Dwinggo; Rawas, Soha
Journal of Engineering Researcher and Lecturer Vol. 3 No. 3 (2024): Regular Issue
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/jerel.v3i3.168

This study conducts a systematic literature review to evaluate the impact of Project-based Learning (PjBL) to vocational education and its effects on developing 21st-century skills. Utilizing the PRISMA framework, the study analyzes 21 articles from an initial pool of 1,036 papers from Scopus and Google Scholar databases. The included literature consists of articles published within the last five years (2019-2024) and systematically examines the findings from relevant studies. The analysis results demonstrate that PjBL is crucial in preparing students for a dynamic, technology-driven workforce by enhancing skills such as learning and innovation, information, media, technology, and life and career skills. Despite challenges in implementing PjBL, such as insufficient technological proficiency, inadequate resources, and misaligned curricula, PjBL offers opportunities to develop more effective pedagogical approaches aligned with industry needs. The study emphasizes the importance of collaboration between educators, policymakers, and industry stakeholders to address these challenges and improve vocational education quality. Additionally, the study identifies the need for further research to bridge existing gaps, particularly in developing information, media, and technology skills. Therefore, PjBL is a potential method for equipping students with essential skills for the ever-evolving job market.

Extended reality for education: Mapping current trends, challenges, and applications Samala, Agariadne Dwinggo; Bojic, Ljubisa; Rawas, Soha; Howard, Natalie-Jane; Arif, Yunifa Miftachul; Tsoy, Dana; Coelho, Diogo Pereira
Jurnal Pendidikan Teknologi Kejuruan Vol 7 No 3 (2024): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v7i3.37623

The advancements in 5G technology and Artificial Intelligence (AI) have accelerated the integration of immersive technologies such as Extended Reality (XR) into educational practices. There is a notable scarcity of studies focusing specifically on the applications and impact of XR in academic settings. Most existing research has concentrated on AR and VR, leaving a gap in understanding the full potential of XR. Addressing these gaps and challenges is crucial for harnessing the full potential of XR in education. This study aims to map and analyze the applications, trends, and educational challenges of XR technology. This study conducts a bibliometric analysis covering XR's application in education from 2018 to 2023, analyzing 32 articles from Scopus sources. Key findings highlight XR's annual growth in research publications, with significant contributions from the United States, China, and Canada. XR enriches education by facilitating immersive simulations, real time interaction with virtual objects, and spatial manipulation in three dimensions. It fosters presence and embodiment in virtual environments, supports practical training through realistic simulations, enhances multi-sensory engagement, promotes collaborative learning environments, and improves accessibility for diverse learners. The main challenges of XR technology include high costs, technical hurdles, regulatory issues, infrastructure limitations, and the need for digital literacy and skills. Addressing these challenges, collaborative efforts among educators, researchers, and industry stakeholders are required. Such collaboration is crucial for harnessing the full potential of XR technology to revolutionize education and prepare learners for a dynamic future.

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

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.

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