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All Journal Journal of Applied Engineering and Technological Science (JAETS)
Duc-Nghia Tran
Vietnam Academy of Science and Technology
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning Chandrakumar Thangavel; Valliammai S E; Amritha P. P; Karthik Chandran; Subrata Chowdhury; Nguyen Thi Thu; Bo Quoc Bao; Duc-Tan Tran; Duc-Nghia Tran; Do Quang Trang
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.6128

Abstract

Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This research aims to measure students’ academic performance on an online learning platform – Moodle Learning Management System (LMS) – using machine learning techniques. Moodle LMS, a popular free and open-source system, has seen significant growth since the COVID-19 lockdown. Many researchers have analyzed student performance in online learning, yet there remains a need to predict academic outcomes effectively. In this study, data were collected from a higher learning institute in Tamil Nadu, and linear regression was applied to predict students' final course outcomes. The analysis, based on students' activity in Moodle LMS across both theory and laboratory courses, helps faculty identify students at risk of failing and adjust instructional methods and assignments accordingly. This approach aims to reduce failure rates by providing timely warnings and encouraging students to improve their engagement with LMS resources.
Efficient Road Surface Classification on Low-Cost Devices Using Vehicle Vibration Data Cong Ngo Van; Duc-Nghia Tran; Thu Bui Thi; Vu Duong Tung; Pham Quang Huy; Manh Tuyen Vi; Duc-Tan Tran
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/2afgj009

Abstract

During road traffic operations, pavement quality directly affects safety, vehicle operating costs, and pavement maintenance activities. Traditional inspection methods are often costly and time-consuming, and they cannot provide continuous data on pavement conditions. This study aims to develop an efficient road-surface classification system capable of real-time operation on low-cost hardware devices. The system uses vibration data collected from vehicles in motion to identify and classify road types with high accuracy and optimized performance. The proposed system employs inertial sensors mounted on vehicles to acquire accelerometer and gyroscope signals and then extracts time-domain statistical features from these signals. To address the main challenge of deploying an effective recognition model in a resource-constrained computing environment, the paper proposes a hybrid feature selection algorithm that combines filter and wrapper methods. This algorithm leverages the fast-processing speed of filter methods and the effective feature selection capability of wrapper methods. The selected feature set is then evaluated using three machine learning models: Random Forest (RF), Gradient Boosting (GBM), and XGBoost. The classification task focuses on three real-world pavement types: smooth asphalt (with less than 10 years of service), degraded asphalt (with more than 15 years of service), and cement concrete pavement. Experimental results show that the proposed feature selection algorithm and classification models achieve high classification performance and fast execution speed. The system attains accuracy higher than 0.95 while reducing computational cost. These findings confirm the feasibility of deploying road-surface classification systems on low-cost devices for real-time pavement monitoring and highlight the importance of appropriate feature selection in balancing system accuracy and performance.
Co-Authors Amritha P. P Bo Quoc Bao Chandrakumar Thangavel Cong Ngo Van Do Quang Trang Duc-Tan Tran Karthik Chandran Manh Tuyen Vi Nguyen Thi Thu Pham Quang Huy Subrata Chowdhury Thu Bui Thi Valliammai S E Vu Duong Tung