This scoping review aims to investigate the current applications of Digital Twin (DT) technology within the field of medical education, evaluating its advantages, limitations, and implications for future research and practice. A comprehensive search was conducted across seven authoritative databases, including PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI), covering the period from the inception of each database until November 20, 2024. Data extraction was carried out using NoteExpress and EndNote software, and studies were selected based on strict inclusion and exclusion criteria. A total of 112 articles were identified in the initial search, of which eight met the criteria for final inclusion in the analysis. These studies predominantly addressed the application of DT in medical imaging education, critical care training, and medical education for individuals with disabilities. The findings reveal that DT technology has shown significant promise in enhancing teaching effectiveness, improving student engagement, and increasing overall satisfaction. However, several limitations were identified, including the nascent stage of the technology, challenges related to system integration, high resource demands, and the difficulties faced by educators in mastering and implementing the technology. Despite these challenges, the application of DT in medical education is progressing, demonstrating substantial potential to advance the modernization of educational practices, improve learning outcomes, and enhance educational efficiency. To fully realize the benefits of DT, further research is needed to address the technological, economic, and pedagogical barriers currently limiting its widespread adoption. Additionally, the development of more effective “digital-physical fusion” teaching models is essential for maximizing the utility and scalability of DT technology in medical education.
The evolution of educational systems, marked by an increasing number of institutions, has prompted the integration of advanced data mining techniques to address the limitations of traditional pedagogical models. Predicting students’ academic performance, derived from large-scale educational data, has emerged as a critical application within educational data mining (EDM), a multidisciplinary field combining education and computational science. As educational institutions seek to enhance student outcomes and reduce the risk of failure, the ability to anticipate academic performance has gained considerable attention. A novel methodology, employing cluster analysis in combination with Bayesian networks, was introduced to predict student performance and classify academic quality. Students were first categorized into two distinct clusters, followed by the use of Bayesian networks to model and predict academic performance within each cluster. The proposed framework was evaluated against existing approaches using several standard performance metrics, demonstrating its superior accuracy and robustness. This method not only enhances predictive capabilities but also provides a valuable tool for early intervention in educational settings. The results underscore the potential of integrating machine learning techniques with educational data to foster more effective and personalized learning environments.
In the context of rapidly advancing digital technology, where touchscreen interactions dominate, the tactile sensory development of children is increasingly compromised. This shift towards digital media can hinder the ability of children to effectively engage with and observe their surroundings. One promising solution to this issue is the integration of art appreciation into educational practices. However, in regions such as Indonesia, there is a noticeable scarcity of comprehensive learning kits aimed at teaching art appreciation. This study addresses this gap by designing and developing an art appreciation learning kit intended for children aged 7 to 11, aiming to teach art appreciation through artful thinking (AT). The kit employs the “see, think, wonder” (STW) thinking routine, a structured three-step process that encourages children to observe art, analyze their observations, and engage with the art through inquiry. The analysis, design, development, implementation, and evaluation (ADDIE) framework was employed as the instructional design model. Additionally, a qualitative research through design (RtD) methodology was adopted to guide the design process and ensure the creation of an innovative educational tool. The developed learning kit integrates physical components, multimedia resources, and hands-on arts and crafts activities that complement the STW routine, thereby fostering deeper engagement and critical thinking skills among young learners. The study emphasizes the value of employing the ADDIE model to assess the learning needs and challenges faced by children, particularly during the STW activity. Collaboration with educators during the design and development phases was identified as crucial for refining the learning kit. Key recommendations include the integration of graphic visualizations, clear demonstrations, and interactive activities to enhance children’s engagement and enthusiasm for art appreciation. The findings offer empirical evidence supporting the effective use of the ADDIE model in educational kit design, providing a valuable reference for future product designers in the educational technology field.
The selection of appropriate digital platforms (DPs) for enhancing the learning process at universities in the Brčko District, Bosnia and Herzegovina (BiH), was explored through the application of fuzzy multi-criteria decision-making (MCDM) methods. This region, with a rich academic heritage, presents a complex landscape of available DPs, each offering varying features and functionalities. Given the diversity of platforms and evaluation criteria, expert-based fuzzy methods were employed to assess and rank potential DPs. Specifically, the Simple Weight Calculation (SiWeC) method was utilized to determine the relative importance of the evaluation criteria, while the Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) method was applied to rank the platforms based on their proximity to an ideal solution. Expert evaluations from three universities in the region indicated that key characteristics of effective DPs include high interactivity, customizable user interfaces, and advanced tools for monitoring student progress. The results highlighted that DPDL 4 emerged as the highest-ranked platform, followed closely by DPDL 1. These findings underscore the significance of integrating interactive and customizable features in DPs to enhance the educational experience. This research contributes to the optimization of e-learning environments at universities in the Brčko District, thus supporting the region’s efforts to improve academic competitiveness and attract prospective students.
The role of cognitive and affective dimensions in mathematical modeling education was investigated, with a particular emphasis on the influence of mathematical values on student engagement and learning outcomes. A mixed-methods approach was employed, incorporating surveys and interviews with students from diverse educational contexts. This approach enabled the quantitative assessment of cognitive competencies alongside qualitative exploration of attitudes and values toward mathematics. It was found that values associated with realism and cognitive engagement significantly shape students’ approaches to mathematical modeling, with cultural differences playing a critical role in the expression and prioritization of these values. The study highlights the necessity of cross-cultural research to elucidate how these values are cultivated across varying educational settings. Furthermore, it is argued that mathematical modeling instruction must be culturally responsive and attuned to the affective dimensions of learning to promote deeper engagement and enhance educational outcomes. The findings underscore the importance of integrating both cognitive and affective factors in the design of educational frameworks, offering insights for optimizing teaching strategies and fostering more effective learning environments.
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