Hoang Son Le
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Hoang Son Le has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Bach-Yen Nguyen-Thi
University of Economics, Ho Chi Minh City 700000, Vietnam
1 shared publication - Duy-Dong Le
University of Economics Ho Chi Minh City, Ho Chi Minh, Vietnam
1 shared publication - Kieu-Chinh Nguyen-Ly
University of Economic Ho Chi Minh City (UEH), Ho Chi Minh City 700000, Vietnam
1 shared publication - Minh Ly Trinh
University of Cuu Long, Vietnam
1 shared publication - Minh-Son Dao
National Institute of Information and Communications Technology
1 shared publication - Thanh-Qui Pham
University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam
1 shared publication
Latest Publications
- Open Access
- 16 January 2024
- Published by Asia Association of Computer Assisted Language Learning in International Journal of TESOL & Education
- Vol. 4 (1) , 1-17
An Investigation of Vocabulary Learning Strategies of ESP Students
English is an indispensable language, especially in the digital age. For students who are not specialized in English vocabulary, understanding effective vocabulary learning strategies becomes crucial, helps increase their chances of being hired, and improves coherence in written and spoken communication. This study investigates the vocabulary learning strategies most and least commonly employed by ESP students. The research involved 59 participants from three majors: business administration, accounting, and tourism. Data collection was carried out through questionnaires and interviews. The findings highlight students' preference for metacognitive strategies and their positive attitude toward technology as a supportive tool for vocabulary acquisition. Additionally, cognitive and memory strategies linked to learned vocabulary were occasionally utilized. The implications of this research extend to teachers, students, and curriculum designers, providing insights for developing appropriate methods to enhance ESP vocabulary learning.
- 16 January 2024
- Published by Asia Association of Computer Assisted Language Learning in International Journal of TESOL & Education
- Vol. 4 (1) , 1-17
Open AccessAn Investigation of Vocabulary Learning Strategies of ESP Students
English is an indispensable language, especially in the digital age. For students who are not specialized in English vocabulary, understanding effective vocabulary learning strategies becomes crucial, helps increase their chances of being hired, and improves coherence in written and spoken communication. This study investigates the vocabulary learning strategies most and least commonly employed by ESP students. The research involved 59 participants from three majors: business administration, accounting, and tourism. Data collection was carried out through questionnaires and interviews. The findings highlight students' preference for metacognitive strategies and their positive attitude toward technology as a supportive tool for vocabulary acquisition. Additionally, cognitive and memory strategies linked to learned vocabulary were occasionally utilized. The implications of this research extend to teachers, students, and curriculum designers, providing insights for developing appropriate methods to enhance ESP vocabulary learning.
- Open Access
- 17 November 2022
- Published by MDPI AG in Algorithms
- Vol. 15 (11) , 434
Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting
The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.
- 17 November 2022
- Published by MDPI AG in Algorithms
- Vol. 15 (11) , 434
Open AccessInsights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting
The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.