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All Journal Al-Hijr: Journal of Adulearn World Journal of World Future Medicine, Health and Nursing
Nam, Le Hoang
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Computer Science & IT Education Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Public Health Veterinary Other

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Development of Automatic Assessment System Based on Machine Learning for Student Learning Evaluation Tu, Bui Minh; Tu, Nguyen Minh; Nam, Le Hoang
Al-Hijr: Journal of Adulearn World Vol. 3 No. 4 (2024)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v3i4.856

Abstract

The rapid advancement of machine learning (ML) has significantly impacted educational technologies, particularly in the area of student assessment. Traditional assessment methods often require substantial time and resources, and may not provide immediate or personalized feedback. An automatic assessment system based on machine learning can offer an efficient solution by automating the evaluation process and providing real-time, data-driven insights into student performance. This study explores the development of an automatic assessment system using machine learning algorithms to evaluate student learning and provide personalized feedback in real-time. A mixed-methods approach was used in this research, combining the design and development of the system with quantitative analysis of its effectiveness. The system was tested on 300 students across different academic disciplines, and data was collected from their interactions with the assessment system. Machine learning algorithms, including natural language processing and classification models, were employed to analyze student responses and generate feedback. The results indicate that the machine learning-based system significantly improved the speed and accuracy of student assessments, providing personalized feedback that helped students identify areas for improvement. The system also reduced the administrative burden on educators. This study concludes that machine learning-based automatic assessment systems are a valuable tool for enhancing the learning evaluation process, offering immediate, scalable, and personalized feedback to students.
Sustainable Health Model: Increasing Universal Access to Health Services in Remote Areas Ardenny, Ardenny; Nam, Le Hoang; Tu, Pham Anh
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i1.1907

Abstract

Access to healthcare services in remote areas remains a significant global challenge, with many populations experiencing disparities in healthcare availability, quality, and affordability. Sustainable health models that ensure universal access to health services are essential for improving public health outcomes in underserved areas. This study investigates the potential for sustainable health models to increase healthcare access in remote regions, focusing on the role of telemedicine, mobile health clinics, and community health workers. The research employs a mixed-methods approach, combining qualitative interviews with healthcare professionals and quantitative data on healthcare access and outcomes in remote communities. The findings indicate that telemedicine platforms have improved healthcare delivery by 40%, while mobile health clinics and trained community health workers have expanded service reach, particularly in geographically isolated areas. Furthermore, community-based health interventions have led to a 30% reduction in preventable diseases in these regions. The study concludes that integrating technology with community-based solutions offers a scalable and effective approach to achieving universal health access in remote areas. However, challenges such as technology infrastructure, resource allocation, and healthcare workforce training need to be addressed to ensure the sustainability of these models.
Co-Authors Ardenny, Ardenny Tu, Bui Minh Tu, Nguyen Minh Tu, Pham Anh