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Education is one among the major service sectors which is continuously growing and contributing significantly to a country’s economy. Students’ positive feedback through word-of-mouth (WOM) is one of the key influences attracting new admissions thereby providing competitive advantage for a university to sustain. There are numerous antecedents identified and implemented to enhance positive WOM and increase intakes in higher education however the students’ choice is still being unpredicted. This study attempts to develop a framework that exemplifies the links between service quality (SQ), relational trust (RT) and students' attitudes toward institutions.

A correlational research design was adopted with a non-probability convenience sampling technique, the data were collected from students in public and private higher education institutions (HEIs) across India. Multivariate regression was the statistical tool used to estimate the path model. SmartPLS 3.0 software performing structural equation modelling (SEM) helped in determining the coefficient values.

The result of the study indicated the magnitude and directional relationship between SQ and trust and justified that they are the key determinants of building a positive attitude towards the institution, enhancing the intention to recommend it among peer groups.

Academic institutions and their public relations departments must prioritize reducing SQ gaps and create strategies to build strong RT among all institution stakeholders to gain a competitive advantage. Socially, this study aims in assisting universities in establishing high-quality education.

The empirical estimation of the relationships between trust, attitude, quality and intention provides the reasons for incorporating and building positive WOM among students’ benefit institutions over the long run.

Online learning is increasingly popular, and educational platforms provide a wealth of courses. Improving course sales is the key to promoting sustainable development of online course platforms. However, limited research has explored the marketing of online courses. We study how to drive online course sales by leveraging teacher information.

We performed an empirical study. We collected data through a crawler and image recognition from Tencent classroom.

Our results show that providing teacher information and profile images helps promote online course sales. However, detailed course descriptions weaken the positive impact of teachers' profile images on online course sales. Furthermore, our study shows an inverted U-shaped relationship between the intensity of smiling in teacher profile photos and online course sales, and teacher descriptions negatively moderate this relationship.

Our study contributes to the research on online course sales and extends the context of the research on smiling as well as the studies of visual and textual information.

The results have practical implications for online course sellers and platforms.

Existing scholarly efforts have explored online courses mainly from an education perspective. More research is needed to advance the understanding of online course sales. Our study advances research in the marketing of online courses.

Stakeholder satisfaction relates closely to service quality and performance of educational organizations. Often, this relationship is addressed in literature through the lens of excellence models, such as the European Foundation for Quality Management (EFQM) model. Nevertheless, limited studies focus on the effect of the EFQM model enablers on stakeholder satisfaction and hardly any in vocational education. To fill this void, the aim of this paper is to investigate the relationship between EFQM enablers (leadership, human resources, processes) and stakeholder satisfaction under the different perspectives of teachers and students in Greek public vocational education.

To address the research purpose, the authors used the case study methodology. The authors used mixed research methods, the focus group method and survey. Specifically, the focus group method used to distill the EFQM enablers related to the Greek context and to develop a questionnaire administered to teachers and students of a vocational education school. From September 2021 to June 2022, 90 teachers and 216 students participated in the survey.

The partial least squares structural equation modeling (PLS-SEM) method was used for data analysis, indicating that leadership and human resources have a positive and significant relationship with the satisfaction of both students and teachers. However, results did not corroborate the relationship between processes and stakeholder satisfaction either from the student’s or the teacher’s perspective.

This paper fills a gap in existing literature by exploring the impact of EFQM enablers on stakeholder satisfaction in vocational education and training (VET), from the perspectives of both teachers and students. The findings assist VET institutions in prioritizing areas for improvement and help companies to develop strategies that consider key stakeholders' needs.

The study explored how PSTs perceived a learning design, using a decision simulator, a self-reflection guide and a peer mentoring guide as tools to mediate reflections on approaches to the teacher’s role. The individual characteristics of PSTs were also considered, including role clarity, self-efficacy and affective commitment, as predictors of the learning design’s perceived relevance.

Informed by earlier research exploring the teacher’s role, a scenario-based simulation was supported by a tool for self-reflection on the results and a peer mentoring tool for the further development of an individual’s teaching role. Using structural equation modelling, the study assessed the statistical strength of the relationships between relevant factors to explore how a decision simulator and associated research-based tools were perceived by preservice teachers (PSTs).

The results indicate that regardless of PSTs’ individual characteristics, the decision simulator and associated peer mentoring tools have the potential to enhance learning and reflection. Therefore, the proposed approach can facilitate peer mentoring and enhance PSTs’ learning potential.

The paper explores the use of innovative approaches to mentoring by introducing peer mentoring grounded in experiences using a decision simulator.

This study explores the consequences of digitalization in the field of education, particularly in relation to teachers’ course processes in higher education institutions. It emphasizes the importance of understanding how information systems (IS) support not only individual tasks but also processes as a whole. The results reveal that process practices have not been considered comprehensively and even core processes may be unseen.

A systematic literature review was conducted to explore the extent to which teachers’ processes are discussed in the literature. A qualitative case study was then conducted at a Finnish higher education institution to identify course processes and their relationships to IS.

Teachers’ processes have scarcely been discussed in the literature, and the process support provided by ISs is remarkably limited. It seems that course processes, which are core to education, are a blind spot in education digitalization. To support evaluating the level of support by IS, novel course process indicators were introduced.

Developing core processes, teachers’ course processes and thesis processes in education field, supports improving service quality. In all industries, organizations should consider whether processes are properly recognized and whether IS support not only individual tasks but also processes as a whole. We recommend recognizing and applying business process management practices to better support teachers’ work and to improve overall efficiency in education.

To the best of our knowledge, this is the first education sector study that attends to teacher’s work as a comprehensive process.

Given continued debates on the potentials of newly emerging artificial intelligence (AI) like generative AI (GenAI), this study aims to contribute to corporal studies by investigating the efficacy of GenAI in improving students writing skills.

A mixed-methods research design with an experimental approach was used to elicit information from 70 undergraduate students studying at a private university. A writing course was designed and used to elicit information from the participants on the efficacy of using ChatGPT in their writing instruction.

After collecting data through experiments and interviews, the result indicates that although ChatGPT may assist students in providing ideas in writing lessons, it may not improve their overall writing skills.

This study provides empirical evidence limited to the scholarly literature on the role of ChatGPT in improving students’ writing skills. This study adds to scholarly discussions on the potential of ChatGPT which has recently sparked debates in academia.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

South Africa lags significantly in mathematics achievement on international benchmarking tests, which has led to several interventions aimed at improving mathematics attainment in the country. Drawing on the theoretical work of Vygotsky, Leontiev and Engeström, this chapter reports on one such initiative that implemented computer technology into disadvantaged schools in the apple growing district of the Western Cape. Contrary to expectations, the object of the lesson became control over students' actions, rather than a mathematical object aimed at developing students' understanding of the subject. The teacher adopted what I call a defensive position in relation to the novel technology, tightening pace and sequencing in these lessons. I draw on Ratnam's work into ‘excessive entitlement’ to illustrate that this teacher's defensive posture regarding technology emanates from a need to exert complete power over the content taught in a lesson and leads her to reject the novel technology in favour of traditional methods. While interviews with the teacher in this study indicated that she felt she promoted student dialogue and more symmetrical power relations in her classes through group work, this is not seen in the data. This is explained in relation to teachers' excessive entitlement to ‘owning’ the knowledge in their classrooms through maintaining control over the rules of the system. I pull on Cultural-Historical Activity Theory (CHAT) to illuminate how the activity of teaching in a classroom affords and constrains what the teacher is able to achieve, often making them feel excessively entitled to push back reform.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

Despite the considerable increase in research on entrepreneurship education, few studies examine the role of entrepreneurship educators. Similarly, most frameworks from entrepreneurship education recognize the educator’s importance in facilitating instruction and assessment, but the factors influencing the educator role are not well understood. According to the identity theory, personal factors including self-efficacy, job satisfaction and personal values influence the perspective of self, significance and anticipations that an individual in this role associates with it, determining their planning and actions. The stronger the role identity the more likely entrepreneurship educators will be in effectively developing their entrepreneurial skills as well as the overall learning experience of their students. The objective of this study is to pinpoint the factors that affect entrepreneurial role identity.

Drawing upon the identity theory, this study developed a theoretical framework and carried out an empirical investigation involving a survey of 289 entrepreneurship educators across the globe. Structural equation modeling (SEM) technique was applied to analyze and explore the factors that impact the identity of the educators in their role as entrepreneurship teachers.

The findings show that the role identity of entrepreneurship educators is significantly influenced by their self-efficacy, job satisfaction and personal values. Among these factors, self-efficacy and job satisfaction have the most significant impacts on how educators perceive their role. The implications of these results and directions for future research are also discussed.

The novelty of the current study is derived from its conceptualization of the antecedents of role perception among entrepreneurship educators. This study stands out as one of the earliest attempts to investigate the factors that shape an individual’s scene of self and professional identity as an entrepreneurship educator. The significance of comprehending the antecedents of role perception lies in the insights it can offer into how educators undertake and execute their role, and consequently, their effectiveness in teaching entrepreneurship.