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p-Index From 2020 - 2025
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All Journal Journal International Inspire Education Technology (JIIET) Journal of World Future Medicine, Health and Nursing
Krit, Pong
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Computer Science & IT Education Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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The Role of Interactive Technologies in Open Distance Learning Pandansari, Purwosiwi; Lek, Siri; Krit, Pong
Journal International Inspire Education Technology Vol. 3 No. 3 (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/jiiet.v3i3.726

Abstract

The expansion of Open Distance Learning (ODL) in recent years has highlighted the need for interactive technologies that support student engagement, knowledge retention, and personalized learning. As ODL environments often lack face-to-face interaction, interactive technologies like virtual classrooms, discussion forums, and gamified learning tools play a critical role in creating immersive learning experiences. This research aims to explore the impact of interactive technologies on student outcomes in ODL, focusing on engagement, satisfaction, and academic performance. A mixed-methods approach was used, combining quantitative surveys with qualitative interviews to capture students’ experiences with interactive tools in ODL. Data was collected from a diverse sample of students enrolled in online programs, and statistical analysis was applied to measure correlations between interactive technology usage and learning outcomes. The qualitative data provided insights into student perspectives on the benefits and challenges of using interactive tools. Findings indicate that students who actively engage with interactive technologies report higher levels of motivation and satisfaction compared to those who rely solely on traditional online resources. Interactive tools also appear to facilitate better knowledge retention and a sense of community among distance learners. However, challenges such as accessibility and technological support remain barriers to effective usage. The study concludes that interactive technologies enhance the ODL experience by promoting active learning and improving overall educational outcomes. Further research is recommended to explore scalable solutions for integrating these tools, particularly in under-resourced settings, to ensure equitable access for all students.
AI-Driven Diagnostic Imaging: Enhancing Early Cancer Detection through Deep Learning Models Ariyanto, Danang; Chai, Napat; Krit, Pong
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i3.2369

Abstract

Early detection is critical for improving cancer survival rates, yet the interpretation of diagnostic images is subject to human error and variability. Artificial intelligence (AI), specifically deep learning, presents a transformative opportunity to enhance diagnostic accuracy and speed. This study aimed to develop and validate a deep learning model to improve the accuracy and efficiency of early-stage cancer detection in radiological images compared to human expert interpretation. A convolutional neural network (CNN) was trained and validated on a curated dataset of over 20,000 mammography images. The model's diagnostic performance was rigorously evaluated using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), against a biopsy-verified ground truth. The AI model achieved an overall accuracy of 97.2%, with a sensitivity of 98.1% and a specificity of 96.5%. The model's performance, with an AUC of 0.98, was comparable to that of senior radiologists and significantly reduced false-negative rates. AI-driven deep learning models are highly effective and reliable tools for augmenting diagnostic imaging. They can significantly enhance early cancer detection, reduce diagnostic errors, and serve as a powerful assistive tool for radiologists in clinical practice.
Co-Authors Chai, Napat Danang Ariyanto Lek, Siri