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The study is aimed at analyzing the perceptions of students and teachers regarding the effectiveness of massive open online courses (MOOCs) on learning efficiency of students and also evaluating MOOCs as an ideal tool for designing a blended model for education.

The analysis was carried out by using the data gathered from the students as well as teachers of University of Mysore, Karnataka, India. Two separate sets of questionnaires were developed for both the categories of respondents. Also, the respondents were required to have prior experience in MOOCs. Further, the collected data was analyzed using statistical package for social sciences (SPSS).

The study showed that MOOCs have a more positive influence on learning efficiency, as opined by both teachers and students. Negative views such as cheating during the assessment, lack of individual attention to students and low teacher-student ratio were also observed.

Many educational institutions view that the MOOCs do not influence learning efficiency and also do not support in achieving their vision. However, this study provides evidence that MOOCs are positively influencing the learning efficiency and also can be employed in a blended model of education so as to promote collaborative learning.

Technology is playing a pivotal role in all fields of life and the education sector is not an exception. It can be rightly said that the technology-based education models such as MOOCs are the need of the hour. This study may help higher education institutions to adopt MOOCs as part of their blended model of education, and, if already adopted, the outcome of the present study will help them to improve the effectiveness of the MOOCs they are offering.

This research aims to explore stakeholder perspectives on Ethiopia’s occupational competency assessment and certification systems.

The study utilized a mixed-methods strategy within a concurrent embedded design and adopted a pragmatic perspective. Data collection involved questionnaires, interviews, and focus group discussions, selecting respondents via purposive sampling for their significant experience and deep understanding of competency assessment. Analytical methods included descriptive and inferential statistics, as well as narrative techniques.

There is a generally positive perception of the value of competency assessments. However, the study finds several major limitations: inadequate candidate competency assessment, lack of skill gap analysis in TVET institutions for improved training, failure to maintain assessment standards, a high candidate-to-assessor ratio, and assessment tools that do not meet occupational standards. These issues show that the existing method misjudges TVET candidates' skills. To increase employer acceptance of competency assessments, the Center of Competence (CoC) agencies should integrate industry expertise, highlight their benefits, and emphasize the importance of training quality and career goals for candidates and trainers.

A study reveals that African nations like Ethiopia, Ghana, South Africa, Rwanda, Morocco, Benin, and Senegal have been implementing competency-based training (CBT) for around two decades, with support from countries like Canada, France, Belgium, Germany, Switzerland, Australia, Luxembourg, and Japan. However, the programs are often inconsistent and disorganized, with little private sector participation. There is a significant difference between the goals of quality assurance entities and the resources allocated. Although competency assessment is a fundamental part of CBT, there is a lack of research demonstrating its practice. Therefore, we conducted this research in Ethiopia, the second most densely populated nation in Africa. The results apply to other comparable nations implementing CBT programs (IIEP-UNESCO, 2021).

The research on stakeholders' perceptions of competency assessment is still in its early stages, with most studies focusing on training quality-related issues. This study expands on our knowledge of occupational competency assessment by analyzing perspectives from a comprehensive stakeholder perspective, considering contextualized assessment practices, addressing stakeholder needs, providing practical implications, and identifying future research directions. Furthermore, it offers valuable perspectives on developing competency-based education in Africa and other regions.

This study aims to analyze the effectiveness and efficiency of research-based community service programs in Indonesia that used government funds in 2017–2021.

The design of this research is a quantitative research method using a data envelopment analysis to evaluate 370 leading universities in Indonesia. Furthermore, six analytical models were considered to compare effectiveness and efficiency between universities. It involved two resource (budget and staff academic involved), three output (intellectual property, prototype and publication) and three outcome variables (economic impact, social impact and capacity building).

The findings showed that several universities are considered necessary, with great potential to increase output and outcome efficiency in community involvement. The study mapped and divided the position of 370 universities for additional information. The effectiveness aspect provides another perspective in assessing the performance of tertiary institutions in Indonesia and can be an option for evaluating research performance to improve the quality of output.

The authors use data from research and community service management information systems used, both the resources used and the results. Efficiency and effectiveness of 370 universities were compared in this study, including comparing their position on the previous assessment with the assessment of the results of this study. Approach to the concept of Mandl et al. (2008) regarding the relationship between input, output and outcome as the main component of the indicators, the authors apply to analyze efficiency and effectiveness.

The purpose of this study is to quantify and evaluate the efficiency of knowledge services public libraries (PLKS) in China and to analyze the factors affecting the efficiency of PLKS. The focus of this study is to explore the characteristics and changing trends of PLKS and how to improve the efficiency of PLKS. The conclusions drawn in this paper are expected to provide important references for academic research and practical exploration in related fields.

This study constructs a three-stage process of PLKS. On this basis, the efficiency is analyzed by using the Super-NSBM model, and the influencing factors of PLKS are analyzed by using OLS. Finally, through the comprehensive interpretation of the measured data and the existing social development conditions, the authors put forward relevant recommendations.

In the first stage, the PLKS in China meets the basic requirements, but there is an obvious waste of resources in the second stage. In addition, China’s public libraries show obvious regional differences in knowledge service efficiency. Finally, it was found that economic and technological factors can increase the efficiency of PLKS, but the increase in the number of university students will reduce the efficiency of PLKS.

In this study, the authors provide an exhaustive deconstruction and explanation of PLKS, which leads to the proposal of a three-stage PLKS efficiency conversion process. Furthermore, the authors identify a diverse set of quantifiable metrics that are readily accessible for open collection. Notably, the authors correlated the efficiency of PLKS with the influencing factors, exploring PLKS from a new perspective and enriching the research related to PLKS.

The study aims to explore the relationship between the SDGs and the environmental and social impacts of university classes. It evaluates the potential contributions of different teaching models to the SDGs, providing insights into sustainable teaching systems.

In-person classes exhibited hotspots in transportation and energy consumption. Scenarios I and II, involving virtual and hybrid classes, increased eutrophication potential and water scarcity due to higher food consumption, negatively impacting SDGs. However, all scenarios showed positive contributions to SDGs 2, 3, 6, 9, 11, 12, 13, 14 and 15.

This research integrates Life Cycle Assessment (LCA) with the United Nations Sustainable Development Goals (SDGs) to assess the environmental and social impacts of classes at the Federal University of São Carlos, Sorocaba campus in Brazil. Three scenarios were analyzed: (I) virtual classes, (II) hybrid classes and (III) in-person classes with improved energy efficiency.

The study primarily focuses on environmental and social impacts, excluding other factors like class quality. Integrating Life Cycle Costing and Life Cycle Sustainability Assessment could provide a more holistic evaluation in the future.

The methodology adopted offers valuable insights for managing the impacts of university performance and aligning teaching systems with the SDGs. It enables institutions to make informed decisions for sustainability in education.

The research emphasizes the importance of considering social impacts alongside environmental ones when assessing sustainability in educational institutions. It encourages universities to engage stakeholders in sustainability efforts.

This research innovatively combines LCA and the SDGs in the context of university education providing a replicable methodology for evaluating and enhancing sustainability in teaching systems and from a more quantitative perspective.

This study aims to explore AI’s potential impact on the broader landscape of higher education in Tanzania. This study contributes to the ongoing discussion of AI’s potential to transform higher education and highlights the ethical considerations and challenges that must be addressed to ensure its successful implementation. This study informs future research and policy decisions in education and technology by providing a detailed understanding of AI’s perceived benefits and challenges in higher education.

A mixed-methods approach was used, which involves collecting and analyzing quantitative and qualitative data to understand the research problem comprehensively. This approach allowed data triangulation and led to a more robust and detailed understanding of this study.

In this study, it was discovered that enhanced assessment, time-saving, personalized learning, improved accessibility and detecting cheating are the perceived benefits of AI as a tool for enhancing learning in higher education, while cost and infrastructure, academic misconduct, data privacy and security, bias and ethical concerns and lack of human interaction are the perceived challenges of AI as a tool for enhancing learning in higher education. Further, it was revealed that students are more accepting of using AI tools in the classroom because they think they are more effective and engaging. On the other hand, faculty were more cautious and skeptical about employing AI tools in the classroom because they worried about how it would affect their teaching methods and job security.

The data collection was not conducted face-to-face. To fully capture respondents’ emotional responses, feelings, facial expressions, reactions, or body language was challenging. However, a sufficient number of individuals who participated were very cooperative, and their knowledge was very beneficial in understanding the topic.

A unique view of this study is a clear understanding of the perceived benefits and challenges of using AI as a tool for enhancing learning in higher education, as well as the variations in these perceptions among students and faculty. By examining the perspectives of both groups, this study provides a comprehensive understanding of the complex role of AI in higher education. Understanding the broader implications of AI in higher education can inform policy decisions and ensure that AI is used responsibly and ethically.

Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education.

This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF).

According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI.

By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education.

The finding of this study should be considered when successful implementation of AI in education is in progress.

The purpose of this study is to analyze the integration of sustainable practices in the strategies and operations of world-class higher education institutions (HEIs) under the theoretical guidance of Max Weber's instrumental and value rationalities.

The results of the Quacquarelli-Symonds World University Ranking, Times Higher Education World University Rankings, THE Impact Rankings and GreenMetric World University Ranking rankings from 2019 to 2022 were paired, and the correlation between them was verified. Institutions with simultaneous occurrence in the four rankings in at least one of the years were also classified. A quantitative and qualitative methodology was used to explore how elite HEIs integrate sustainable practices into their operations and strategies, under the theoretical guidance of Max Weber's instrumental and value rationalities. Furthermore, multivariate regression models with supervised data mining techniques were applied, using the SMOReg algorithm on 368 instances with multiple attributes, to predict the numerical value of sustainability in the rankings. Coefficients were assigned to variables to determine their relative importance in predicting rankings.

The results of this study suggest that although many HEIs demonstrate a commitment to sustainability, this rarely translates into improvements in traditional rankings, indicating a disconnect between sustainable practices and global academic recognition.

The research has limitations, including the analysis being restricted to data from specific rankings between 2019 and 2022, which may limit generalization to future editions or rankings. The predictive models used selected data and, therefore, cannot cover the full complexity of metrics from other rankings. Furthermore, internal factors of HEIs were not considered, and the correlations identified do not imply direct causality. The limited sample and potential methodological biases, together with the heterogeneity of the rankings, restrict the generalization of the results. These limitations should be considered in future studies.

The theoretical contributions of this study include an in-depth understanding of the intersection between academic excellence and environmental and social responsibility. From a management perspective, guidance is provided on integrating sustainability into HEI strategies to enhance visibility and classification in global rankings, while maintaining academic integrity and commitment to sustainability.

This highlights the importance of reassessing academic rankings criteria to include sustainability assessments, thereby encouraging institutions to adopt practices that genuinely contribute to global sustainable development.

The originality lies in the predictive analysis between these rankings, examining the link between the level of sustainability of an HEI and its classification as a World Class University. Furthermore, it combines theories of rationality with the analysis of sustainability integration in elite HEIs, introducing new analytical perspectives that can influence future educational policies and institutional practices.

This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.

The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.

The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.

This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

This study aims to provide an overview of the state-of-the-art in efficiency measurement within higher education (HE). Specifically, it seeks to gather all relevant articles on the topic and subsequently categorize these studies using a flowchart based on two core aspects of the topic.

This study employs bibliometric and content analyses to conduct a systematic literature review. The Preferred Reporting Items for Systematic Review (PRISMA) framework is used to identify the search protocol, followed by analyses to classify and categorize articles.

The bibliometric analysis identifies prominent themes, methodologies and literature gaps. The content findings highlight key insights on higher educational institution (HEI) efficiency, including organizational structures, services and operational activities.

This research contributes to the existing knowledge by synthesizing global literature on HEI’s efficiency. Utilizing the flowchart developed by the authors, the study captures the state-of-the-art based on two critical aspects: methodologies and content. Insights from the analysis and subsequent classification of previous literature provide valuable directions for future research.