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Numerous challenges in education emerge as our technology-driven society rapidly evolves and manifests more exigent requirements from engineering professionals. Higher education, nonetheless, seems to adapt to such requirements at an unequal speed, generating some tensions between industry and higher education institutions. The purpose of this paper is to share the experiences obtained through a process of assessment and redesign of a large enrollment course of programming from which the authors developed a systematic approach for course design/redesign.

A mixed method approach was deployed for data gathering and evaluation, consisting of close-ended surveys, open-ended questionnaires, information matrices and state of the art compilation. Triangulation of the information offered clear data about the necessity of curriculum redesign; therefore, a new programming course curriculum encompassed with relevant necessities in engineering and science was developed.

The authors produced a coherent and dynamic systematic path for assessment and design/redesign of course curriculum, which the authors find extremely helpful to improve negotiation processes inside higher education institutions, as it can be implemented to improve any large enrollment course curricula in engineering and science.

By following the systematic path for assessment and design/redesign of curricula the authors developed, higher education systems could embark more efficiently in the ever-challenging process of adapt their courses and programs to tackle the upcoming demands of our society.

So far, a systematic path for assessment and design/redesign of course curriculum was not published, and it supports the improvement of pedagogical approaches in academic institutions.

The natural control, feedback, stimuli and protection of these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation system was created that integrated natural interaction assisted by electroencephalogram (EEG), which enabled the movements in the virtual environment and real wheelchair. For blind wheelchair operator patients, this paper involved of expounding the proper methodology. For educating the value of life and independence of blind wheelchair users, outcomes have proven that virtual reality (VR) with EEG signals has that potential.

Individuals face numerous challenges with many disorders, particularly when multiple dysfunctions are diagnosed and especially for visually effected wheelchair users. This scenario, in reality, creates in a degree of incapacity on the part of the wheelchair user in terms of performing simple activities. Based on their specific medical needs, confined patients are treated in a modified method. Independent navigation is secured for individuals with vision and motor disabilities. There is a necessity for communication which justifies the use of VR in this navigation situation. For the effective integration of locomotion besides, it must be under natural guidance. EEG, which uses random brain impulses, has made significant progress in the field of health. The custom of an automated audio announcement system modified to have the help of VR and EEG for the training of locomotion and individualized interaction of wheelchair users with visual disability is demonstrated in this study through an experiment. Enabling the patients who were otherwise deemed incapacitated to participate in social activities, as the aim was to have efficient connections.

To protect their life straightaway and to report all these disputes, the military system should have high speed, more precise portable prototype device for nursing the soldier health, recognition of solider location and report about health sharing system to the concerned system. Field programmable gate array (FPGA)-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals, the soldier’s health is observed on systematic bases. By emerging Verilog hardware description language (HDL) programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t the whole work is approved in a Vivado Design Suite. Classification of different abnormalities and cloud storage of EEG along with the type of abnormalities, artifact elimination, abnormalities identification based on feature extraction, exist in the segment of suggested architecture. Irregularity circumstances are noticed through developed prototype system and alert the physically challenged (PHC) individual via an audio announcement. An actual method for eradicating motion artifacts from EEG signals that have anomalies in the PHC person’s brain has been established, and the established system is a portable device that can deliver differences in brain signal variation intensity. Primarily the EEG signals can be taken and the undesirable artifact can be detached, later structures can be mined by discrete wavelet transform these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as multirate support vector machine classifiers when the features have been extracted using a combination of hidden Markov model (HMM) and Gaussian mixture model (GMM). Intended for capable declaration about action taken by a blind person, these result signals are protected in storage devices and conveyed to the controller. Pretending daily motion schedules allows the pretentious EEG signals to be caught. Aimed at the validation of planned system, the database can be used and continued with numerous recorded signals of EEG. The projected strategy executes better in terms of re-storing theta, delta, alpha and beta complexes of the original EEG with less alteration and a higher signal to noise ratio (SNR) value of the EEG signal, which illustrates in the quantitative analysis. The projected method used Verilog HDL and MATLAB software for both formation and authorization of results to yield improved results. Since from the achieved results, it is initiated that 32% enhancement in SNR, 14% in mean squared error (MSE) and 65% enhancement in recognition of anomalies, hence design is effectively certified and proved for standard EEG signals data sets on FPGA.

The proposed system can be used in military applications as it is high speed and excellent precise in terms of identification of abnormality, the developed system is portable and very precise. FPGA-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals the soldier health is observed in systematic bases. The proposed system is developed using Verilog HDL programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t and synthesised using in Vivado Design Suite software tool.

ChatGPT from OpenAI is an amazing example of machine learning technology. This technology has now become an important issue for high-tech plagiarism concern. Indeed, there are many concerns about using this tool, perhaps using other technologies to make ChatGPT safer. Non-fungible tokens (NFTs) may be a way out. This paper aims to discuss such an alternative.

To preventing with high-tech plagiarism created by the ChatGPT tool two ways can help schools, universities and scientific centers to prevent academic plagiarism: first, by banning ChatGPT and adjusting teaching styles, and second, by using detecting AI-produced content. In this viewpoint, the authors suggest a third way that can be a way out.

NFTs technology has the ability to add a non-fungibility feature to any digital object (image, text or video). Therefore, any text produced by artificial intelligence tools can be given a specific NFT code. With this work, the authors add a feature to texts produced by artificial intelligence, that is, the non-fungibility feature.

In this viewpoint, how and why NFTs may be a usefully added value in preventing acts of high-tech plagiarism on ChatGPT is discussed.

A majority of higher education institutions (HEIs) in Oman, and internationally, have adopted English as the language of education, driven by its power and its globally accepted status as the language of knowledge and communication. Such an internationalisation policy has been inadequately evaluated to examine its actual effects. This paper aims at analysing the existing literature with a view to hypothesise the effects of adopting English as a medium of instruction (EMI) on establishing intellectual capital in the Omani context.

The paper employs a case study design that draws on data generated through a systematic review of 94 peer-reviewed papers that are synthesised using thematic analysis.

The findings indicate that EMI negatively affects the optimal creation of intellectual capital through limiting access to HE, hindering knowledge transfer, impeding Omanis' employability and hindering faculty's professional growth. EMI leads HEIs to mirror the supplying countries' cultures in terms of materials, ideologies and standards. It affects teaching and research quality, training and communication, the sense of equity, belonging and self-worth amongst students and the relationships amongst faculty members. It also increases reliance on external stakeholders.

The paper highlights the interconnection between the forms of intellectual capital and how some components are antecedents to the creation of the intellectual capital forms. It establishes the moderating role the language of instruction plays in relation to the three sub forms of intellectual capital in higher education.

The paper calls for maximising higher education intellectual capital through adopting bilingual rather than monolingual higher education. It calls upon policymakers to revisit the assumptions underlying higher education systems in order to optimise their outcomes.

The paper is the first one that sheds light on the role of language in intellectual capital construction. Such a moderating role has received almost no attention in the higher education literature that is largely busy quantifying its outcomes rather than ensuring they are actually sustainably generated.

Supply chains are under increased scrutiny as consumers have become aware of the dark side of downstream production practices. Many articles and exposés have led consumers to be more conscious of purchasing products from companies who source materials in a socially responsible and ethical manner. As a result, business-to-business (B2B) and business-to-consumers (B2C) companies are under increased pressure to source raw materials in a transparent and ethical way. Because of the associated costs, companies then look to benefit from increased brand equity by promoting to consumers how ethical their products are. The purpose of this study is to look at the case of the food industry to analyze sustainability messaging on Twitter in both B2B and B2C companies to determine which of the dimensions of sustainability (people, profit, planet) are being emphasized.

In this study, two published dictionaries were combined to capture the three dimensions of “sustainability,” and these scales were then used to analyze Twitter posts. This study created a unique software package to classify, mine, collect and analyze Twitter data. This study used these tools to analyze 246,386 Twitter posts within a sample of 39 leading B2C and B2B food companies over a 10-year period (2012–2021) to explore brand messaging and engagement rate.

This research revealed several interesting results. These include how B2B companies have emphasized the employee (people) dimensions of sustainability, while B2C companies have had a more balanced approach that overall has prioritized the economic dimension (profit) of sustainability. The data on audience engagement revealed a mismatch between the types of sustainability messaging B2B companies and B2C companies in the food industry are posting and what engages audiences.

This study fills several gaps, including analyzing how B2B and B2C companies use sustainability language in their social media brand management, as well as looking at which dimensions of sustainability they emphasize and which ones engage audiences the most. This research is also novel in combining multiple existing scales under one project to analyze the triple bottom line in the analysis of qualitative texts.

As the number of school-aged children living outside their country of birth continues to rise, domestic governments should examine whether their education systems have the appropriate policies and practices in place to provide equitable accountability for migrant students. Specifically, domestic governments should work to ensure immigrant students who are not yet proficient in the host country's language are being systematically identified and provided the language supports necessary to engage in their education.

Over the past two decades, the US has been able to hone its approach to educating immigrant children. Drawing from this experience, this brief will show how the federal government has used education policy to increase access and accountability for immigrant children enrolled in US schools. Specifically, this brief will highlight the policies the US has adopted to facilitate uniform and objective procedures in how schools:

• identify immigrant students with linguistic needs upon enrollment;

• assess their language skills upon enrollment to create a baseline; and

• systemically and continually support and assess immigrant students' linguistic needs to ensure they can engage with age-appropriate academic content.

identify immigrant students with linguistic needs upon enrollment;

assess their language skills upon enrollment to create a baseline; and

systemically and continually support and assess immigrant students' linguistic needs to ensure they can engage with age-appropriate academic content.

By highlighting these practices and policies, this brief will show key steps other domestic governments can take to increase the visibility of and accountability over the education of migrant children by ensuring their linguistic needs are proactively addressed.

This research aims to contribute to the literature on sustainable hospitality and tourism by applying social network analysis to identify sustainable tourism business networks and untangle the role of cognitive and geographical proximity in their formation.

Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant information from the internet.

The results support the existence of geographical clusters of tourist accommodation providers that are linked by strong cognitive proximity based on sustainability principles that are well communicated via their websites. This does not appear to be greenwashing because companies that have agreed on sustainability principles have also implemented concrete actions and tend to signal these actions through a variety of sustainability certifications.

The results may guide tourism managers and policymakers in developing tourism initiatives directed at the creation of fruitful collaborations between similarly oriented organizations and methods to support clusters of sustainable tourism accommodation. Identifying sustainable tourism networks may assist in the identification of potential actors of change, fueling a widespread transition toward sustainability.

In this study, the authors adopted an innovative methodology to detect sustainability-oriented tourism business networks. Additionally, to the best of the authors’ knowledge, this study is one of the first to simultaneously explore the cognitive and geographical connections between tourism businesses.

Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.

This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.

The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.

The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.

This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.

The purpose of this study is to determine what technological strategies were used within each of the phases of the ADDIE framework when developing content for professional training. The study also examined the affordances of those technologies in training.

A PRISMA systematic review methodology (Moher et al., 2015) was utilized to answer the four questions guiding this study. Specifically, the PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols (PRISMA-P, Moher et al., 2015) was used to direct each stage of the research, from the literature review to the conclusion. In addition, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA principles; Liberati et al., 2009) are used to guide the article selection process.

The findings reveal that the majority of the studies were in healthcare (36%) and education (24%) and used an online format (65%). There was a wide distribution of ADDIE used with technology across the globe. The coding for the benefits of technology use in the development of the training solution revealed four trends: 1) usability, 2) learning approaches, 3) learner experience and 4) financial.

This systematic review only examined articles published in English, which may bias the findings to a Western understanding of how technology is used within the ADDIE framework. Furthermore, the study examined only peer-review academic articles from scholarly journals and conferences. While this provided a high level of assurance about the quality of the studies, it does not include other reports directly from training providers and other organizations.

These findings can be used as a springboard for training providers, scholars, funders and practitioners, providing rigorous insight into how technology has been used within the ADDIE framework, the types of technology, and the benefits of using technology. This insight can be used when designing future training solutions with a better understanding of how technology can support learning.

This study provides insight into the uses of technology in training. Many of these findings and uses of technology within ADDIE can also transfer to other aspects of society.

This study is unique in that it provides the scholarly community with the first systematic review to examine what technological strategies were used within each of the phases of the ADDIE structure and how these technologies provided benefits to developing a training solution.