Universities play a crucial role in supporting sustainable development. In recent decades, indicator-based assessment tools have emerged to quantify universities’ efforts towards sustainability. The most widely known is the UI GreenMetric World University Rankings (UI-GWUR): In our paper, we examine the sustainability performance of the three greenest Hungarian universities. The University of Pécs, the University of Szeged and the University of Sopron were among the top 200 higher education institutions (HEIs) in the UI-GWUR in 2023, which proves that they have successfully integrated sustainable development into the components of their system. The aim of the paper is to identify the sustainability measures implemented by the three-top Hungarian HEIs. Their experiences shed light on how it is possible to move forward in the UI GWUR for a Hungarian higher education institution. In order to evaluate the sustainability efforts of the universities, the UI GWUR database was first examined. The websites and sustainability reports of the three universities were also analyzed to gain insight into their activities. Identifying the sustainability actions of the three institutions will help other universities to successfully plan and implement their sustainability initiatives. In the last part of our paper, we evaluate how the three Hungarian universities communicate sustainability through their websites. The results show that advancement in the UI Green Metric World University Rankings primarily requires conscious planning, which means a deeper understanding of the ranking methodology on the one hand, and a clear strategy creation and implementation on the other hand.

Teachers are instrumental in advancing the cognitive and motor skills of children with autism. Despite their importance, the incorporation of both educators and robotic aids in the educational frameworks of specialized schools and centers is infrequent. Extensive research has been conducted to evaluate the impact of robotic assistance on the learning outcomes for children with autism. This study investigates the effects of the Furhat robot on the educational experiences of autistic children in schools, analyzing its utility both with and without the presence of teachers. Interviews with educators were carried out to gauge the effectiveness of implementing Furhat robots in these settings. Data collected from sessions with autistic children were analyzed using ANOVA tests, offering insights into the Furhat Social Robot’s potential as a significant tool for fostering engagement and interaction. The findings highlight the robot’s effectiveness in enhancing social interaction and engagement, thereby contributing to the ongoing discussion on how social robots can improve the developmental progress and well-being of children with autism. Moreover, this paper underlines the innovative aspects of our proposed model and its wider implications. By presenting specific quantitative outcomes, our aim is to extend the reach of our findings to a broader audience. Ultimately, this research delineates significant contributions to the understanding of social robots, such as Furhat, in improving the overall well-being and developmental trajectories of children with autism.

E-learning has become an integral part of higher education, significantly influencing the teaching and learning landscape. This study investigates the impact of student characteristics such as gender, grade, and major on E-learning satisfaction. Utilizing Structural Equation Modeling (SEM) and collecting data through 527 valid questionnaires from Nanjing Normal University students, this research reveals the nuanced relationships between these variables and E-learning satisfaction. The findings indicate that gender, grade, and major significantly and positively impact student satisfaction with E-learning, highlighting the need for tailored E-learning resources to meet diverse student needs. The study underscores the importance of continuous improvement in E-learning resources and platforms to enhance student satisfaction. This research contributes to the understanding of effective E-learning strategies in higher education institutions.

Performance Management is a major concern to various stakeholders in Education System, it is considered to be key driver to improve school effectiveness and learning quality. However, the complexity of education Systems, has made it challenging to apply an effective PM model. This study paper introduces a maturity model with six dimensions, fifteen Capability Areas and forty-two Best-Practices to assess education systems’ organizational capacity for performance management. It provides deep insights into their structural and functional characteristics and serves as a framework for decision-makers to identify and implement missing practices while enhancing existing ones. The maturity model was developed following the Design Science Research methodology to ensure both rigor and relevance. A bottom-up approach guided its design, integrating insights from extensive literature reviews and lessons learned from benchmark countries. The evaluation process employed a qualitative approach, using focus groups with a carefully selected cohort of academics, experts, and practitioners. The Moroccan case study serves as part of the “Reflection and Learning” phase, providing an initial test for the model and paving the way for further empirical research. Future studies will aim to test, refine, and extend the model, facilitating its application across diverse educational contexts.

This paper conducts a bibliometric visual analysis of the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) in education, using CiteSpace software. Drawing on data from the Web of Science, the study explores research trends and influential works related to UTAUT from 2008 to 2023. It highlights the growing use of educational technologies such as mobile learning and virtual reality tools. The analysis reveals the most cited articles, journals, and key institutions involved in UTAUT research. Furthermore, keyword analysis identifies research hot spots, such as artificial intelligence and behavioral intentions. This study contributes to the understanding of how UTAUT has been used to predict technology adoption in education and provides recommendations for future research directions based on emerging trends in the digital learning environment.