This study aims to investigate the academic community’s engagement with research on green policies within higher education institutions. The study examines the evolving landscape of green policy research in universities, seeking to elucidate trends in academic interest, key contributors, and thematic developments. Employing bibliometric analysis, this research scrutinizes publications indexed in the Web of Science database from 1994 to 2024, with a particular focus on keywords, co-authorship networks, and institutional affiliations. The findings indicate a notable increase in publications, particularly post-2016, reflecting a transition from broad conceptual themes to more specific applications of green policies, including sustainable management practices and performance evaluation. Central themes identified include “green”, “sustainability”, “performance”, and “management”, highlighting a shift from theoretical exploration to practical implementation. Prominent contributors, such as Wang Y and Zhang Y, alongside institutions like Tsinghua University, have significantly advanced the field. Furthermore, the study underscores a robust correlation between the growth in scientific output and the emergence of specialized sustainability journals, indicating an escalating academic demand for focused publication platforms. The results suggest that research on green policies in universities is increasingly characterized by interdisciplinary collaboration and the integration of innovative technologies and methodologies to effectively address sustainability challenges. The field of green policy application in higher education is rapidly expanding, with a well-connected, collaborative research community generating impactful work that harmonizes modern technologies and methodologies to confront sustainability issues at both global and local levels.
This study investigates the differences in the factors contributing to school dropout between rural and urban educational institutions in Romania, focusing on individual, family, school, and community dimensions. A sample of 557 participants, including educational directors, teachers, and administrators, was surveyed to assess the prevalence of various dropout causes. The Mann-Whitney U test was employed to identify statistically significant differences between rural and urban schools in specific dropout factors. The findings indicate that urban schools report higher incidences of individual-level issues such as substance abuse, juvenile delinquency, teenage pregnancy, and health-related problems. At the family level, urban institutions were more likely to encounter students with incarcerated parents or those placed in alternative care. School-related factors also varied, with urban schools being characterised by larger class sizes and insufficient access to counselling and guidance services, while rural schools were more affected by early school start times. In the community dimension, urban schools faced greater challenges with negative peer influences and a lack of educational facilities near students’ homes. These results suggest that the causes of dropout in urban settings are more complex, necessitating tailored interventions and resources. It is recommended that context-specific strategies be developed to address the distinct dropout factors in both rural and urban environments, thereby supporting more inclusive and effective educational policies in Romania.
Returnee faculty play a pivotal role in international knowledge transfer and the advancement of scientific research within domestic universities. However, the effectiveness of returnees in enhancing institutional research performance remains inadequately understood. This study seeks to quantify and examine the collaborative academic networks of returnee faculty, assessing their impact on research output and funding performance. Based on an extensive academic dataset, comprehensive scientific collaboration networks (SCN) of returnees were constructed and empirically analyzed to elucidate the influence mechanisms underpinning research performance. The findings indicate that the presence of returnee faculty substantially enhances overall publication output and funding acquisition. Further, within the SCN of returnees, both academic influence and network expansion positively correlate with research productivity and funding success, whereas an increase in cooperation density appears to exert a negative effect. Additionally, the evolution of these collaboration networks was explored, revealing that returnees’ SCN display lower similarity and retention over time compared to those of native faculty. These insights offer a valuable theoretical basis for improving the management and integration of returnee faculty and optimizing the allocation of higher education resources, thereby fostering more effective pathways for enhancing institutional research outcomes.
The rapid advancement of artificial intelligence (AI) technology has significantly impacted the higher education sector, creating an urgent demand for composite talents equipped with interdisciplinary knowledge. The cultivation of “AI + X” talents, combining AI expertise with various domain-specific skills, has been increasingly recognized as a critical educational goal. This study explores the development and implementation of teaching practices aimed at fostering such composite talents in higher education institutions. The growing integration of AI into diverse fields necessitates the construction of a robust curriculum that combines AI technology with other disciplines, thereby enhancing students’ interdisciplinary capabilities. A comprehensive literature review and experimental research methods were employed to analyze both domestic and international trends in AI-related talent development. Additionally, a predictive model of student learning performance was developed through exploratory data analysis (EDA) and machine learning (ML), with results validating the efficacy of linear regression models in performance prediction. The research identifies key strategies for enhancing teaching practices, including reinforcing theoretical and technological knowledge, promoting personalized and practical teaching approaches, strengthening foundational disciplinary learning, and encouraging cross-disciplinary synergies. These strategies were designed to enhance students’ critical thinking and practical competencies, with the aim of preparing them for the complex challenges of a rapidly evolving workforce. Furthermore, the paper discusses how AI-driven educational reforms can support the development of key industries, such as smart cities, smart finance, and the broader digital economy. The findings suggest that integrating AI technology into educational practices is essential for the effective cultivation of “AI + X” talents. However, significant challenges remain, including the scarcity of educational resources and the need for more contemporary teaching methodologies. Further research is required to refine talent training systems and to optimize institutional mechanisms, ensuring that higher education institutions can meet the demands of future technological and economic transformations. Through sustained educational innovation, it is envisaged that a new generation of innovative and versatile professionals will be equipped to contribute to societal advancement.
An Integer Linear Programming (ILP) model was proposed to optimize academic timetables, with a focus on the School of Management at Western Galilee Academic College. The model was designed to address the dual challenges of course scheduling and faculty availability while incorporating a minor structural adjustment to enhance computational efficiency and accelerate convergence, particularly for large-scale problems. By employing this model, optimal scheduling solutions were generated within minutes, even in scenarios involving over 200 classes and 100 lecturers. The approach effectively minimizes planning time, identifies unavoidable scheduling conflicts, and highlights unschedulable classes due to constraint violations. Furthermore, the model provides actionable insights into staffing requirements, ensuring a comprehensive resource allocation strategy. Results from the application of the model during the 2023 winter semester demonstrated its capability to efficiently schedule 236 classes across multiple programs and instructional modalities. The method achieved adherence to predefined constraints, optimized the utilization of institutional resources, and enhanced overall scheduling efficiency. This case study underscores the potential of the proposed ILP framework to streamline academic timetabling processes, particularly in environments with diverse programmatic needs and complex resource interdependencies. The findings indicate that the model can be readily adapted to other academic institutions seeking to improve the effectiveness and precision of their scheduling systems.
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