Search results

This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.

A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.

The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.

The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.

This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.

This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.

The existing literature contains few references on the better adaptors of online distance education amongst STEM (read as science, technology, engineering and mathematics) and non-STEM (composed of humanities, social science and commerce) study groups in an Indian peri-urban context. The study's objective is to determine the better adaptor amongst these two study groups in online distance learning in higher education systems in an Indian peri-urban context.

The investigation was carried out prior to COVID-19 and during the pandemic. The inquiry is triangulated in nature with a disproportionate stratified random sampling approach used to pick 312 post-graduate students (STEM = 135 and non-STEM = 177) from a peri-urban higher education institute in West Bengal, India, using the “Raosoft” scale. Given the prevailing social distance norms, 235 samples of respondents from 312 students were evaluated via telephonic/online interviews during the COVID-19 period. The data were analysed using SPSS 22.

This study's investigations reveal that the STEM respondents have better digital profiles, better basic computing and Internet knowledge and greater digital usage for academic purposes before the pandemic times than the non-STEM group. This prior digital exposure has enabled the STEM group to cope with regular online distance education during the pandemic more quickly than the non-STEM group, as evidenced by their regular attendance in online classes and their greater awareness of its utilitarian role than the other group.

The study offers a way forward direction to evolve with more inclusive online distance learning in peri-urban Indian regions.

The information technology (IT) sector has been seen as central to society's transformation to a more just and sustainable society, which underlines teachers’ responsibility to foster engineers who can contribute specifically to such ends. This study aims to report an effort to significantly update an existing engineering programme in IT with this ambition and to analyse the effects and challenges associated with the transformation.

This study is based on a combination of action-oriented research based on implementing key changes to the curriculum; empirical investigations including surveys and interviews with students and teachers, and analysis of these; and a science and technology studies-inspired analysis.

Respondents were generally positive towards adding topics relating to sustainability. However, in the unmaking of traditional engineering subjects, changes created a conflict between core versus soft subjects in which the core subjects tended to gain the upper hand. This conflict can be turned into productive discussions by focusing on what kinds of engineers the authors’ educate and how students can be introduced to societal problems as an integrated part of their education.

This study can be helpful for educators in the engineering domain to support them in their efforts to transition from a (narrow) focus on traditional disciplines to one where the bettering of society is at the core.

This study provides a novel approach to the transformation of engineering education through a theoretical analysis seldom used in studies of higher education on a novel case study.

Online privacy pertains to an individual’s capacity to regulate and oversee the gathering and distribution of online information. Conversely, online privacy concern (OPC) pertains to the protection of personal information, along with the worries or convictions concerning potential risks and unfavorable outcomes associated with its collection, utilization and distribution. With a holistic approach to these relationships, this study aims to model the relationships between digital literacy (DL), digital data security awareness (DDSA) and OPC and how these relationships vary by gender.

The participants of this study are 2,835 university students. Data collection tools in the study consist of personal information form and three different scales. Partial least squares (PLS), structural equation modeling (SEM) and multi-group analysis (MGA) were used to test the framework determined in the context of the research purpose and to validate the proposed hypotheses.

DL has a direct and positive effect on digital data security awareness (DDSA), and DDSA has a positive effect on OPC. According to the MGA results, the hypothesis put forward in both male and female sub-samples was supported. The effect of DDSA on OPC is higher for males.

This study highlights the positive role of DL and perception of data security on OPC. In addition, MGA findings by gender reveal some differences between men and women.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2023-0122

In this study a survey with a questionnaire was used to track the learning situation and core competencies of students in electrical engineering and computer science at the University of Science and Technology. A determination of areas that need to be strengthened is used as a reference in the cultivation of core competency in higher education.

A total of 320 junior students in electrical engineering and computer science were surveyed and given questionnaires. A total of 219 valid questionnaires were collected for a paired sample t-test, to find and compare changes in core competencies of the group over a period of three years.

Higher education institutions are presently more inclined to competency-oriented education, training and learning. This establishes good relevant practices, enhances core competencies in students and improves their chances of satisfactory future employment. In this study it was found that the general core competencies of students were better than their professional core competencies, and the self-reported core competencies of freshman students were better than those of the junior students. The results showed that core competencies did not grow with the years. This result is most likely due to a gain of knowledge which makes them more able to understand their own shortcomings.

Most studies investigating the core competencies of higher education students are cross-sectional studies. This study follows students over a period of three years and provides the results of a longitudinal survey of a group of students' core competencies.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

This chapter outlines how the academic field in Germany, particularly universities as institutions of research and education, became a constitutive element in the process of digitalization. Institutions in higher education created long-lasting networks not only by scientific relations in the academic field but also by the establishment of business-oriented and application-oriented disciplines. This chapter focuses on the case of business information systems (BIS) and reconstructs the structural conditions under which BIS evolved from an initially marginal data-centric subfield of business administration into an independent discipline. This discipline has had a major influence on the transformation of work processes in recent decades and is an important player in the digital transformation in Germany. This chapter therefore also outlines the far-reaching implications of the paths taken, which are eventually not limited to the academic field but manifested in transformed work processes in almost all professional fields.

This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.

The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.

Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.

This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.

Augmented reality (AR) and virtual reality (VR) are becoming increasingly interwoven into people's daily lives, with applications in fields such as science, engineering, medical, the arts, education, finance and business. While much study has been conducted in the field of VR and AR in a variety of areas, less attention has been paid to online education. The purpose of this research is to conduct a systematic review of studies on AR and VR, as well as its applications in many disciplines of education, with a special emphasis on online education.

The data were gathered from the SCOPUS database under the subject “Augmented and Virtual Reality Applications in Online Education.” The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format has been used to summarize the available literature and research in academics throughout the world. First, systematic literature reviews and later bibliometric analysis were conducted for performance and science mapping analysis.

The research adds to the body of knowledge about the role of AR and VR in online education and its future prospects. AR and VR have demonstrated a substantial contribution to facilitating teaching in a variety of fields, including engineering, medicine, nursing and chemistry. Additionally, this research indicates that AR and VR can be utilized to teach various areas and specialties. Additionally, it can be used in other aspects of online education, such as admissions, industry visits, training, internationalization and specific courses for students with disabilities.

This research lays the framework for educational institutions, legislators and researchers to investigate additional applications of VR and AR. Though this research suggests some possible uses for AR and VR in the online education system, more applications can be integrated into the online education system to fully leverage the potential of AR and VR worldwide. AR and VR have the potential to be a very cost-effective method of delivering online education on a wide scale in the future.

The article explores the applicability of VR and AR in education using bibliometric analysis. This is one of the first studies to do an assessment of VR and AR, evaluating their strengths, drawbacks and current applications. Future application areas are also identified as a result of the investigation.

In a technological era driven by coding, programming and artificial intelligence (AI), there is more need than ever to develop computing skills and knowledge for non-specialist students. Nonetheless, the literature on computer science teaching methods of non-scientific majors is not as comprehensive as that of scientific ones.

Pedagogically, the authors designed and implemented prototyping from John Dewey's pragmatic epistemological lens. Using a mixed methods approach, the authors tested the effectiveness and efficiency of this approach within the same course over four semesters across four academic years.

As an epistemological pedagogic device, prototyping facilitated learning by doing and experimenting and stimulated graduate students' self-directed learning, engagement and their overall ownership of the learning and teaching process, changing their role from being merely passive recipients of “strange, awkward and unfamiliar” knowledge to active constructors of “relevant and exciting” content knowledge. Such a change was reflected in the significant progress students made, driven by their commitment, motivation and enthusiasm, irrespective of their prior knowledge and age (Generations X, Y and Z). Prototyping also served as an avenue for a “Deweyian Reflection”, where graduate students, after internalizing the acquired computing skills and knowledge, started transferring such skills and knowledge to their professions (journalism and public relations (PR)) and daily practices.

Findings from this study will add to the literature review on this subject matter and will inform future case studies in computer science education for graduate students from non-scientific backgrounds.

This paper reveals that learning by doing/experimenting needs to be accompanied by in-depth reflection to enable students to transfer the acquired knowledge and skills to other settings and contexts beyond that of the lesson, task, or project in hand.

There is little research published on introductory programming courses offered to non-specialized students (i.e. students from non-scientific backgrounds). This study contributes to the body of research on how to effectively engage these students in programming courses informed by John Dewey's pragmatic lens/epistemological lens.