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All Journal Al-Hijr: Journal of Adulearn World Techno Agriculturae Studium of Research
Tu, Bui Minh
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Languange, Linguistic, Communication & Media 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.
WASTEWATER TREATMENT TECHNOLOGY FOR AGRICULTURAL IRRIGATION IN SPAIN Tu, Bui Minh; Peng, Nam; Mai, Nguyen Thi
Techno Agriculturae Studium of Research Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i3.1998

Abstract

Water scarcity in Spain has become a major challenge for the agricultural sector, thus encouraging the use of treated wastewater as an alternative source of irrigation. This study aims to evaluate the effectiveness of wastewater treatment technology on agricultural productivity, environmental impact, and farmers’ income in Spain. The research method used is an experimental quantitative design with data collection from wastewater treatment plants, field tests on soil quality, as well as interviews and questionnaires to farmers in the research area. The results show that the use of treated wastewater increases crop yields by 10-15% and reduces the use of chemical fertilizers by 20%, without causing a negative impact on soil and groundwater quality. Farmers’ acceptance of this technology is also quite high, driven by real economic benefits. In conclusion, wastewater treatment technology in Spain has the potential to be a sustainable solution to the water crisis in the agricultural sector, although more research is needed to understand the long-term impact on the environment.
Co-Authors Nam, Le Hoang Nguyen Thi Mai, Nguyen Thi Peng, Nam Tu, Nguyen Minh