Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal International Journal of Educational Narratives Journal of Computer Science Advancements Journal of Selvicoltura Asean Journal of Midwifery History and Philosophy
Olivia Davis
Simon Fraser University
Agriculture, Biological Sciences & Forestry Computer Science & IT Education Nursing Public Health Social Sciences Other

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The Impact of Using Online Learning Platforms on Student Learning Motivation Emma Clark; Olivia Davis; Sara Al-Jabri
International Journal of Educational Narratives Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v3i2.2148

Abstract

Background. The rapid growth of online learning platforms has significantly impacted educational practices globally, particularly in enhancing student learning motivation. Purpose. This study explores the effect of utilizing online learning platforms on students’ motivation to learn, considering their engagement, learning strategies, and academic performance. The primary aim of this research is to analyze how the use of such platforms influences students’ intrinsic and extrinsic motivation within the context of various educational settings. Method. This study adopts a quantitative research approach, using surveys and questionnaires administered to a sample of students from different educational institutions. Data collected were analyzed using descriptive statistics and inferential analysis to determine the relationship between online learning platform usage and students’ motivation levels. Results. The findings reveal a positive correlation between online learning platform usage and increased motivation, particularly in terms of fostering self-regulation, engagement, and a greater sense of autonomy in learning. Students reported higher motivation to participate in lessons and complete assignments when using these platforms. Conclusion. In conclusion, integrating online learning platforms into traditional education methods can significantly enhance students’ learning motivation, supporting both their academic success and personal growth. Future studies should focus on long-term effects and the comparative benefits of different platforms.  
From Isolation to Innovation: Narrative Self-Study of Teachers Adopting Digital Pedagogies in Remote Canadian Regions Olivia Davis; Benjamin White; Charlotte Brown
International Journal of Educational Narratives Vol. 3 No. 3 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v3i3.2209

Abstract

Background. Teachers in remote Canadian regions have historically faced challenges related to geographic isolation, limited access to professional development, and infrastructural disparities. The COVID-19 pandemic accelerated the demand for digital pedagogies, forcing educators in these contexts to rapidly adopt unfamiliar technologies and reconfigure their instructional practices. Purpose. This study investigates how teachers in remote areas navigated this transition through a narrative self-study lens. Method. Using qualitative methodology, five educators from rural provinces in Northern Canada engaged in self-reflective journaling and peer dialogue over a nine-month period. Thematic analysis of the narratives revealed key tensions between professional isolation and digital empowerment, as well as shifts in teacher identity, agency, and pedagogical innovation. Results. Participants described initial resistance, technological uncertainty, and emotional fatigue, which gradually evolved into adaptive strategies, collaborative learning, and renewed professional purpose. The findings highlight how digital transformation, though initially disruptive, served as a catalyst for reflective growth and community-building in marginalized teaching environments. Conclusion. The study concludes that narrative self-study can be a powerful tool for supporting teacher resilience, agency, and innovation, especially in geographically and technologically constrained settings.  
The Impact of Selective Logging on Forest Structure and Function Olivia Davis; Ethan Thompson; Emma Clark
Journal of Selvicoltura Asean Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i6.1674

Abstract

Selective logging is a prevalent forest management practice aimed at balancing timber production and conservation. However, its effects on forest structure and function remain a topic of significant concern. This study aims to evaluate the impact of selective logging on the biodiversity, biomass, and ecological functions of forest ecosystems. We employed a comparative analysis method, where forest plots subjected to selective logging were compared to undisturbed control plots. Data were collected on tree species diversity, density, and biomass, alongside assessments of soil health and microclimate conditions. Our findings indicate that selective logging significantly alters forest structure by reducing tree density and species diversity, leading to an overall decline in biomass. Additionally, changes in soil composition and moisture levels were observed, negatively affecting the forest's ecological functions. The results underscore the importance of adopting sustainable logging practices that mitigate adverse effects on forest ecosystems. In conclusion, while selective logging can provide economic benefits, its detrimental impacts on forest structure and function necessitate careful management and monitoring to preserve biodiversity and ecosystem health.
The Transformation of the TNI Navy Hospital to a Public Service Agency in the Context of Validating the TNI Navy Health Organization: Literature Review Tiya Setiadi; Indriani MR Hutagalung; Ahmad Faisol; Olivia Davis
Journal of Midwifery History and Philosophy Vol. 2 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jmhp.v2i1.3748

Abstract

Background. Public sector reforms in Indonesia are driving government hospitals toward the Public Service Agency (BLU) model to enhance flexibility and service quality. As a key provider for military personnel and the national system, the Indonesian Navy Hospital (RSAL) is part of this significant management shift. Purpose. This study aims to analyze the transformation process of RSAL into a BLU institution and explore its implications for the validation of the Navy’s health organization. Method. The research utilizes a Systematic Literature Review (SLR) following the PRISMA approach, analyzing academic publications, government regulations, and hospital management data. Results. The BLU model improves financial management and operational efficiency. However, challenges remain in governance and human resources, as the rigid military structure often complicates the adoption of the flexible systems required by the BLU model. Conclusion. Successful transformation requires a comprehensive strategy focused on human resource development, policy alignment, and improved organizational governance to bridge the gap between military and BLU systems.
THE AI ENERGY DILEMMA: FINDING THE MIDDLE GROUND BETWEEN HIGH PERFORMANCE AND ECO-FRIENDLINESS James Scott; Olivia Davis; Jessica Green
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.3337

Abstract

The exponential escalation of computational requirements for training and deploying Deep Learning models has precipitated an energy crisis, necessitating a critical reevaluation of the trade-off between algorithmic performance and environmental sustainability. This study aims to reconcile these conflicting demands by developing and validating a novel Dynamic Energy-Aware Pruning (DEAP) framework designed to maximize inference efficiency without compromising predictive accuracy. Employing a rigorous quantitative experimental design, we benchmarked state-of-the-art neural architectures, including ResNet-50 and Large Language Models (LLMs), across diverse hardware environments. The research utilized real-time telemetry to measure total energy consumption (Joules), thermal output, and carbon intensity () against standard accuracy metrics. Empirical results demonstrate that the proposed framework achieved a 42% reduction in energy consumption and stabilized hardware thermals, while maintaining predictive performance within a strict 1.5% non-inferiority margin compared to dense baselines. We definitively conclude that algorithmic sparsity effectively decouples high-level intelligence from excessive power usage, establishing a viable engineering paradigm for “Green AI” that aligns the trajectory of artificial intelligence with global decarbonization targets.
Co-Authors Ahmad Faisol Benjamin White Charlotte Brown Emma Clark Ethan Thompson Indriani MR Hutagalung James Scott Jessica Green Sara Al-Jabri Tiya Setiadi