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Building upon Internet of Things (IoT), Internet of Vehicles (IoV) and Social Internet of Things (SIoT), Social Internet of Vehicles (SIoV) is the latest development in the field. SIoV allows vehicles to socialize among themselves and share information of common interests. The increasing popularity of the SIoV concept demands more research to explore its great potential. However, very few studies have systemically investigated this concept to provide a comprehensive view for readers. The paper aims to discuss this issue.

This paper discusses the perspectives of the utilizations and limitations of SIoV, setting forward a systematic and epistemological framework.

The authors summarize the benefits of SIoV from four information-management perspectives: safety management, traffic control and convenience, productivity improvement and commercialization and exploring the factors inhibiting the development of SIoV from the following seven aspects: standardization, adaptability, scalability, infrastructure, lack of application, privacy and security.

The paper lays a solid foundation for researchers to find possible solutions to address the challenges to SIoV and provides valuable insights for practitioners who are interested in adopting SIoV initiatives.

The research aims at examining educators’ perceptions, attitudes and behavioral intentions toward learning analytics (LA) and the role of self-instruction within the proposed model for LA adoption.

A quantitative approach is utilized in which a questionnaire is designed as a tool for data collection and partial least squares structural equation modeling (PLS-SEM) is used for data analysis and model testing.

Results show that performance expectancy and effort expectancy have a significant effect on educators’ attitudes, which in turn significantly affect educators’ behavioral intentions. Self-instruction mediates the relationship between educators’ attitudes and behavioral intentions. The attitude towards LA mediates the relationship between LA performance expectancy and educators’ self-instruction. The research model explains 54% of the variance in learning analysis adoption.

Findings open a path for research on pedagogical factors affecting LA adoption and guide education managers toward facilitating LA adoption. The tested model contributes to LA and teaching and learning literature by highlighting the role of educators’ self-instruction in LA adoption.

The post-globalization epoch has placed academic institutions internationally in competitive situations where knowledgeable, effective and capable decisions have come to provide the comparative edge. Academia has turned to explicit – and even conceptualizing on tacit – knowledge management to elaborate a systematic approach to develop and sustain the intellectual capital needed to succeed, in response to the employment market demand for its products. To be able to do that, you must be able to visualize your organization as consisting of nothing but knowledge and knowledge flows. The use of web-based technologies in academic institutions for their diverse practices has been widespread in colleges and universities for several decades. These applications include surveying stakeholders, assessing classes, reporting on faculty development and assurance of learning (AoL) data to mention a few. Further advances have led to the integration of applications that not only enable the sharing of knowledge but which also support the reporting requirements necessary to obtain and retain accreditation, for example; likewise, satisfy the supply of intellectual capital to the employment marketplace. The purpose of this paper is to portray the relationship between AoL and accreditations at large in business schools, with the particular articulation of a modus operandi and relevant model that could facilitate curriculum improvement likewise.

Observational research (or field research) is a type of correlational (i.e. non-experimental) research in which a researcher observes ongoing behavior. There are a variety of types of observational research, each of which has both strengths and weaknesses. A select set of business schools and leading accreditation agencies have been observed and reported upon in pertinence with the expected practices and modus operandi toward assuring learning.

The use of web-based technologies in academic institutions for their diverse practices has been widespread in colleges and universities for several decades. These applications include surveying stakeholders, assessing classes, reporting on faculty development and AoL data to mention a few. Further advances have led to the integration of applications that not only enable the sharing of knowledge but which also support the reporting requirements necessary to obtain and retain accreditation; likewise, satisfy the supply of intellectual capital to the employment marketplace. In this paper, the author aims to portray the relationship between AoL and assessment at large with real-life examples and approaches.

Observational research types are organized by the extent to which an experimenter intrudes upon or controls the environment. Observational research is particularly prevalent in the social sciences. It is a social research technique that involves the direct observation of phenomena in their natural setting. This differentiates it from experimental research in which a quasi-artificial environment is created to control for spurious factors, and where at least one of the variables is manipulated as part of the experiment. Henceforth, other research methods may be engaged in to quantify and investigate the phenomenon of AoL vs international practices reported upon as per internal and external forces acting on business schools.

The diversity of approaches followed by business schools and encouraged by accreditations agencies is immense and at times may be connected to the choices such schools make as to how they ought to measure the learning curves of their constituents. Herein, a practical AoL approach is transcribed, as liaised with assessment and evaluation data.

Academia has turned to explicit – and even conceptualizing on tacit – knowledge management to elaborate a systematic approach to develop and sustain the intellectual capital needed to succeed, in response to the employment market demand for its academic products and services. To be able to do that, you must be able to visualize your organization as consisting of nothing but knowledge and knowledge flows.

This research is conceptualized upon as per the international standards and expectations from the field with an Association to Advance Collegiate Schools of Business framework in mind and aims to link AoL to curriculum management and through e-Assessment in a practical manner.

The potential capabilities and benefits that learning analytics can provide are not fully utilized. A current stream of research suggests that learning analytics has more to offer for continuous improvement of higher education institutions. This study aims to explore the opportunities that data analytics stand to offer higher education and the challenges that plays down its role, adoption and usage in different areas of higher education institutions.

This study adopts a systematic literature review approach in answering the research questions. The critical role of learning analytics and the exploratory nature of research questions justify the use of systematic literature review. The current study used systematic research process adapted and presented by Hallinger (2013) to be used in social sciences in general and in educational leadership and management in particular. A standard process of finding relevant articles and examining reference lists is followed using articles from higher education which is the research context.

An examination of the literature showed that the majority of studies within the sample of articles are empirical representing 53 per cent, 32 per cent are conceptual, while only 15 per cent of the articles are a systematic literature review. Results also show that 58 per cent of the articles are teaching and learning related, 34 per cent are management related, while only 8 per cent are research related. Several challenges and opportunities of learning analytics in the three areas highlighted are presented and discussed.

The benefits and challenges of learning analytics are numerous and scattered in the literature. In this study, a typology related to different educational domains is developed to shed light on the benefits and challenges of learning analytics within particular higher education areas that are relevant to specific stakeholders. Benefits and challenges of learning analytics are classified into being management related, teaching and learning related and research related.

Innovation and entrepreneurship are economic drivers promoting competition and growth among organizations throughout the world, many of which would not exist without well-established new product development processes coupled with intentional and strategic focus on research and development. New product development processes, such as the lean start-up methodology and design thinking, are well-known and thriving as a result of empirically grounded research efforts. Unfortunately, educational institutions and educational researchers, alike, are lagging when it comes to new program/degree development processes. Although the quantity of new degree offerings has increased substantially over the past several decades (in particular for multidisciplinary, interdisciplinary and transdisciplinary programs), limited research has been conducted to document key procedures associated with the creation of new degree programs. The purpose of this study is to show one approach to how students can be involved within the new program development process.

This approach uses participatory research, wherein students act as researchers and actively participate in the data collection and analysis process. Under the umbrella of participatory research, the study uses photovoice, photoelicitation and focus groups for collecting qualitative data.

Results suggest that students in one transdisciplinary studies in technology program value the following key attributes: learning style (agency and choice, active hands-on learning and real-world applications) and learning context (technology and design-focused assignments, integration of humanities and self-selected disciplines of interest).

Recommendations are provided for various higher education benefactors of the user-generated data, including administration, faculty, marketing, recruitment, advisors and the students themselves.

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.

Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.

Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.

This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.

Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.