Hoa-Huy Nguyen

National accreditation systems generate large volumes of evidence, but these data are rarely reused to examine conditions that may support technology-enabled learning (TEL), inclusive access and learner success. This study analyses programme-level accreditation results for 2502 accredited higher education programmes in Vietnam evaluated between 2017 and 2025. Nine accreditation criteria were mapped, a priori, to three formative domains: technology and support capacity, learner-success support, and access and inclusion safeguards. Descriptive analysis and K-means clustering identified four differentiated profiles: baseline enabling conditions (46.8%), access and inclusion strength (25.3%), learner-support intensive (14.5%), and technology-support intensive (13.4%). The profiles also differed on separate accreditation indicators concerning the monitoring of graduate employment, student research and stakeholder satisfaction, although effect sizes were small. The findings show how existing accreditation evidence can provide a low-cost diagnostic layer for post-accreditation improvement while complementing, not replacing, dedicated TEL benchmarking and professional judgement.

In an era where diversity and digitalization significantly influence higher education, understanding and adapting to various learning preferences is crucial. This study comprehensively analyzes 394 scholarly articles from 1984 to 2022 using bibliometric methods, providing a dynamic overview of the research patterns in learning styles within higher education. We identified four stages of development during this period: 1984–1995 (Low-interest), 1996–2005 (Early development), 2006–2018 (Development), and 2019–2022 (Intensification). Our analysis highlights that the United States, the United Kingdom, and Australia were the top three leading publishers of research on learning styles in higher education. The results reveal three main topics of publications: educational technology, learning environments, and subject behaviors. This research not only identifies emerging research topics but also underscores the importance of adapting instructional strategies to diverse learning styles to enhance educational outcomes in higher education.

Background: Plagiarism by researchers and college students in Vietnam has become a major concern for publishers. Many cases of master’s theses of graduate students in Vietnam being cancelled or their diplomas being revoked for plagiarism are recorded, and some scientists also have been warned or criticized for plagiarism or self-plagiarism. Objectives: The purpose of this study was to analyse the use in educational research of 13 popular text-matching software packages at universities in Vietnam. Methods: The study was based on semistructured interviews of 104 researchers from 18 universities in Vietnam with reference to the use of text-matching software by the researchers. Results: The three most commonly used text-matching software packages were Turnitin, DoIT, and iThenticate. Three-fourths of the 18 universities employ text-matching software and although 17 out of 104 researchers were unfamiliar with such software. Universities in Vietnam primarily require plagiarism checks for master’s theses (79 out of 104 responses) and doctoral dissertations (72 out of 104 responses). Out of 104 participants, 32 use them for graduate theses or project reports, and 45 use them for research papers and project reports. Conclusions: Many universities in Vietnam are yet to specify the requirements for use of text-matching software, and most researchers and students use it only when prompted by publishers or institutions. Researchers in educational science typically lack the financial resources and the requisite skills for using text-matching software.

This paper has analyzed research trends on personalized learning by bibliometric analysis method through a study of 928 articles from the Scopus database. The following issues are investigated: (1) Development scale, growth trajectory and geographical distribution of the research; (2) Outstanding authors and works on Personalized Learning; (3) Outstanding magazines and books on the topic; (4) Key themes found in these documents, and (5) Prominent methods/technologies used for personalized learning. Research results show that personalized learning is a fascinating topic in education and has been overgrown in recent years. Many researches on personalized learning comes from countries such as the United States and China. Our bibliometric analysis has revealed the main themes in the research works on Personalized Learning, such as artificial intelligence, learning style, and learning technology. The research has observed cognitive aspects of learners like knowledge level, learning style, preferences, etc. In most cases, the recommended tools and methods combined the content-based filtering, collaborative filtering, ontological approaches, etc. In addition, future research goals, difficulties, and concerns are highlighted in our work by examining the trends in several personalized learning elements.

Learning styles play a vital role in determining an individual student's best learning methods and suitable types of learning materials. Based on a survey that collects information about learners' learning styles in offline and online environments, chosen from Felder-Silverman Learning Style Model (FSLM) and David Kolb Learning Style Model, we have developed a model to group students based on their characteristics. Specifically, the data collected from 546 learners in 2 universities in Vietnam are put into clustering using the K-Means algorithm, then labelled and classified by Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithm, and finally evaluated based on precision, recall, and F1 score metric to find the number of suitable groups. The results show a new learning model with four different learning styles, each corresponding to a category in the FSLM and David Kolb model, with values on the accuracy, precision, recall, and f1 score equaling 96%. With the strength of combining the theory of all three different learning style models, along with a machine learning model with high accuracy on a rather large data set, the research results promise to make a positive contribution to the problem of personalized learning content, helping learners have the most effective learning experience.