Virtual reality (VR) has emerged as a transformative technology in education, enabling immersive simulations that foster student engagement and improve knowledge retention. This study presents a bibliometric analysis of global trends, research patterns, and challenges related to the integration of VR in educational contexts. Data were collected from the Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Emerging Sources Citation Index (ESCI) within the Web of Science Core Collection, including only articles also indexed in Scopus to ensure high-quality and comprehensive coverage. Of the 1220 articles published between 2010 and 2024, 97 met the inclusion criteria based on relevance, accessibility, and methodological rigor. We performed keyword analysis, citation analysis, and thematic mapping using RStudio and the Bibliometrix package. The findings indicate that the United States, China, and the United Kingdom are the most active contributors to VR research in education. VR has been applied across diverse learning modalities, enriching both formal and informal educational environments. However, its widespread adoption faces several barriers, including high implementation costs, health and safety concerns, limited curricular integration, and insufficient educator training. Proposed solutions include strategic partnerships with technology providers and the use of cloud-based infrastructures to reduce costs and complexity. This study offers actionable insights for educators, policymakers, and developers, providing a robust, data-driven foundation for the effective and equitable integration of VR in education.

Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group ( N = 30) and the control group ( N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills.

The integration of technology in education has driven significant innovation, with virtual reality (VR) emerging as a powerful tool to enhance the learning experience. VR allows students to engage in immersive and interactive environments that go beyond traditional teaching methods, supporting visualization of complex concepts and experiential learning. Although VR has shown success in various disciplines such as medicine, engineering, and design, its applications in other fields remain unexplored. This study addresses this gap by conducting a comprehensive bibliometric analysis of VR in education, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and utilizing multiple indexes in the Web of Science Core Collection and data from leading publishers. The analysis revealed key trends, such as Springer Nature's dominance in publishing VR-related research and the geographical distribution of VR studies, with the United States, China, and the United Kingdom as major contributors. Despite VR's potential, challenges such as high hardware costs, integration difficulties, and educator training needs hinder widespread adoption. The study also identifies potential solutions, including the development of affordable VR tools and better curriculum integration. The findings offer valuable insights for policymakers, educators, and technology developers, helping to guide more effective implementation and wider adoption of VR in diverse educational contexts.

Manual inventory recording and heuristic ordering practices remain common among Micro, Small, and Medium Enterprises (MSMEs), often leading to inaccurate demand estimation, excessive holding costs, and stockouts. This study develops and evaluates a web-based inventory information system that integrates Autoregressive Integrated Moving Average (ARIMA) forecasting with the Economic Order Quantity (EOQ) model to improve decision accuracy and cost efficiency. The system uses CodeIgniter 3 and MySQL and incorporates a Python-based time-series forecasting engine. Historical sales data were modeled using ARIMA, and the optimal specification was selected based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The ARIMA(1,1,1) model achieved a Mean Absolute Percentage Error (MAPE) of 8.47%, indicating high forecasting accuracy for operational planning. The forecasted annual demand was integrated into the EOQ framework to determine the optimal order quantity, Reorder Point (ROP), and probabilistic Safety Stock. A one-year cost simulation demonstrated that the EOQ-based policy reduced total inventory costs by 22.73% compared with the existing approach. Functional validation through Black-Box testing confirmed full compliance with specified requirements. These findings demonstrate that integrating predictive analytics with classical inventory optimization enhances operational efficiency and reduces total inventory cost. The system provides a practical, data-driven inventory management framework for MSMEs undergoing digital transformation.

Previous research has predominantly examined the effectiveness of learning applications through experimental designs, yet few have explored how these technologies shape students’ internal cognitive and psychological skills. This study makes a novel contribution by embedding a smart learning application within an integrated psychological-cognitive framework, i.e., prior knowledge, metacognition, cognitive engagement, and self-regulated learning, to explain the development of critical thinking disposition. The learning process was designed using the case method, involving 369 undergraduates in the Faculty of Engineering at Universitas Negeri Padang, Indonesia. Data were collected via an online survey and analyzed through Structural Equation Modeling (SEM) with SmartPLS 3 and Fuzzy C-Means Clustering (FCM) with JASP. Results of the direct effect analysis show that prior knowledge and metacognitive do not directly influence critical thinking, while cognitive engagement and self-regulated learning exert significant positive effects. Indirect effect analysis further indicates that prior knowledge and metacognitive significantly affect critical thinking when mediated by cognitive engagement and self-regulated learning. Clustering analysis reveals that most respondents engage with the application at a moderate level. Therefore, targeted interventions such as scaffolding strategies to guide learning processes, adaptive feedback to foster metacognitive monitoring, and gamification elements to sustain motivation are essential for enhancing engagement, reducing passive use, and maximizing the educational impact of smart learning technologies.

UAVs with RTK technology offer high-accuracy aerial mapping, with RTK Radio and RTK NTRIP as two correction methods affecting orthomosaic precision. While previous studies compared these methods in terrestrial surveys, their impact on UAV-based orthomosaics remains underexplored. This research evaluates and compares the accuracy of UAV-derived orthomosaics using RTK Radio and RTK NTRIP to provide practical guidance for optimal mapping applications. This research combined UAV RTK photogrammetry and GIS to produce high-precision aerial maps in two areas of Padang City with different topographies. Data were collected using DJI Mavic 3 Enterprise RTK with RTK Radio and RTK NTRIP techniques, supported by GCPs, BMs, and satellite imagery, then processed into orthomosaics for analysis. Accuracy was assessed using ICPs and statistical measures like RMSE to compare the spatial precision of both correction methods under varying terrain conditions. The results show a difference in mapping accuracy between RTK Radio and RTK NTRIP aerial photographs, with RTK Radio achieving 0.683 accuracy and 0.450 RMSE, while RTK NTRIP reached 0.563 accuracy and 0.371 RMSE. RTK Radio is more suitable for areas lacking CORS access but still requires GCP verification. RTK NTRIP performs better in urban areas with strong CORS networks. Combining both methods with adequate GCPs provides an effective solution for mapping in varied or densely vegetated terrains.