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The Influence of the Student Facilitator and Explaining Type Cooperative Learning Model on Students' Mathematics Learning Outcomes Yuniarti, Yuniarti; Nguyễn, Minh Tuấn
Interval: Indonesian Journal of Mathematical Education Vol. 2 No. 2 (2024): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/ijome.v2i2.1186

Public participation in legislative process during the state of emergency in Vietnam Thi My, Hanh Dang; Nguyen, Minh Tuan
Ius Humani. Jornal do direito v. 14 n. 2 (2025): Ius Humani. Revista de Derecho
Publisher : Universidad Hemisferios

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31207/ih.v14i2.432

This research attempts to comprehensively analyze the role and requirements of people's participation in the legislative process during Vietnam's state of emergency. Using conventional, trusted legal research methods, most notably desk review of legislation and case law analysis, this research affirms that, during a state of emergency, procedures involving public participation cannot follow the usual process. However, a pandemic must never be used as an excuse to erode democratic principles, and only when citizens actively take part in regular circumstances will they have the motivation to engage during emergencies. With Vietnam, many legal documents were issued by executive agencies instead of the National Assembly. Therefore, although the timely issuance of policies and laws was ensured during the pandemic, the principles of the legislative process were not fully upheld or implemented, leading to many debates regarding the legality of the documents issued during this period. In that sense, this research focuses on analyzing the current legal system and several typical cases of law enforcement in Vietnam and other countries to identify the legal gap and propose solutions to improve the legal framework on this issue in Vietnam.

From Problems to Progress: Improving Mathematics Learning Outcomes through Problem-Solving Instruction Nguyễn, Minh Tuấn; Alorgbey, Bernard
Interval: Indonesian Journal of Mathematical Education Vol. 3 No. 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/ijome.v3i2.2519

Purpose of the study: The aim of this research is to improve students' learning outcomes in Mathematics by using the Problem Solving method. Methodology: The research conducted used Classroom Action Research. Data were obtained from qualitative and quantitative data. Data collection techniques included observation, testing, documentation, and interviews. The data analysis method used both qualitative and quantitative data. Main Findings: Based on the results of data analysis, it is known that, after using the Problem Solving method, student learning outcomes have increased. This can be seen from the results of the pre-test and post-test given to students, which always increased in each cycle. The increase in student learning outcomes in cycle 1 was 71.88% and in cycle II 87.10%. There was an increase in the completeness of student learning outcomes by 5.31%. Novelty/Originality of this study: This study introduces a structured problem-solving instructional model implemented through classroom action research to regularly improve students' mathematics learning outcomes. Unlike previous studies, it integrates iterative reflection cycles with authentic classroom problems, providing practical evidence on how problem-solving instruction directly enhances students' engagement, conceptual understanding, and achievement.

Artificial Intelligence, Transformational Leadership, and Job Performance: Mediating Role of Job Engagement and Moderating Role of Work Passion Nguyen, Phuong Thao; Nguyen, Phuc Quy Thanh; Nguyen, Minh Tuan
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1266

This study investigates the relationships among Artificial Intelligence (AI), perceived usefulness of AI, transformational leadership, job engagement, and job performance, with the moderating role of work passion. Drawing on the Job Demands–Resources (JD–R) model and the Technology Acceptance Model (TAM), the study proposes a research model explaining how technological and leadership resources jointly influence employee performance in the context of digital transformation. A quantitative approach was employed, with data collected through an online survey of 345 employees at five leading joint-stock commercial banks in Vietnam. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test the proposed hypotheses. The findings reveal that perceived usefulness of AI is the strongest indirect predictor of job performance through the mediating role of job engagement. The results also confirm that transformational leadership significantly enhances employee engagement, particularly through inspirational motivation and individualized consideration. Artificial Intelligence, as an organizational resource, further strengthens engagement by reducing workload and supporting decision-making processes. Furthermore, work passion plays a moderating role in the relationship between job engagement and job performance, with harmonious passion amplifying this relationship while obsessive passion may reduce its marginal effect. These findings highlight the importance of integrating AI applications with effective leadership practices to foster employee engagement and improve job performance in modern digital organizations.

The Influence of the Student Facilitator and Explaining Type Cooperative Learning Model on Students' Mathematics Learning Outcomes Yuniarti, Yuniarti; Nguyễn, Minh Tuấn
jurnal matematika Vol 2 No 2 (2024): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/ijome.v2i2.1186

From Problems to Progress: Improving Mathematics Learning Outcomes through Problem-Solving Instruction Nguyễn, Minh Tuấn; Alorgbey, Bernard
jurnal matematika Vol 3 No 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/ijome.v3i2.2519

Purpose of the study: The aim of this research is to improve students' learning outcomes in Mathematics by using the Problem Solving method. Methodology: The research conducted used Classroom Action Research. Data were obtained from qualitative and quantitative data. Data collection techniques included observation, testing, documentation, and interviews. The data analysis method used both qualitative and quantitative data. Main Findings: Based on the results of data analysis, it is known that, after using the Problem Solving method, student learning outcomes have increased. This can be seen from the results of the pre-test and post-test given to students, which always increased in each cycle. The increase in student learning outcomes in cycle 1 was 71.88% and in cycle II 87.10%. There was an increase in the completeness of student learning outcomes by 5.31%. Novelty/Originality of this study: This study introduces a structured problem-solving instructional model implemented through classroom action research to regularly improve students' mathematics learning outcomes. Unlike previous studies, it integrates iterative reflection cycles with authentic classroom problems, providing practical evidence on how problem-solving instruction directly enhances students' engagement, conceptual understanding, and achievement.

Sepsis detection using biomarkers and machine learning Vu, Tuan Anh; Bac, Dang Hoai; Nguyen, Minh Tuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1286-1297

Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.

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