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All Journal JTH: Journal of Technology and Health
Elenara Vassanti
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Health Professions Medicine & Pharmacology Nursing

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THE IMPACT OF LEARNING MANAGEMENT SYSTEMS (LMS) ON STUDENT PERFORMANCE IN BRAZILIAN SCHOOLS Elenara Vassanti
JTH: Journal of Technology and Health Vol. 1 No. 3 (2024): January: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v1i3.424

Abstract

This study aims to evaluate the impact of Learning Management System (LMS) usage on student academic performance in Brazilian secondary schools. A quantitative descriptive approach was applied to analyze students’ interaction data within LMS platforms and correlate it with their academic grades. Data were collected from 180 students across three public schools using LMS activity logs and online questionnaires. The results revealed a significant positive correlation between LMS usage frequency—especially assignment submissions and discussion forums—and student grade averages. Consistent LMS engagement contributed to improved learning outcomes, particularly when supported by active teacher facilitation and well-integrated digital instructional design. The study recommends enhancing teacher training and digital infrastructure as key strategies to optimize LMS effectiveness in Brazil’s secondary education context.
UTILIZATION OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) FOR LANDSLIDE RISK MAPPING IN MOUNTAINOUS AREAS Elenara Vassanti
JTH: Journal of Technology and Health Vol. 3 No. 2 (2025): October: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v3i2.482

Abstract

This study aims to map landslide disaster risk in mountainous areas by integrating Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) method. Using a spatially-based quantitative-descriptive approach, the study evaluates seven key parameters: slope, lithology, rainfall, land use, elevation, population density, and road proximity. Data were analyzed through spatial layer overlay and validated against 50 historical landslide points. Results indicate that 34.5% of the area falls under high to very high-risk zones. ROC validation yielded an Area Under Curve (AUC) score of 0.82, indicating high model accuracy. The study also assesses community risk perception and adaptive capacity, revealing a notable discrepancy between subjective awareness and objective risk maps. Few respondents were aware of official risk maps, and community preparedness remained limited. The novelty of this research lies in its systematic integration of physical, social, and adaptive capacity aspects within a spatial framework. The findings recommend the development of participatory, GIS-based early warning systems aligned with local policy frameworks. This model shows strong potential for replication in other disaster-prone mountainous regions globally.
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