Smart Grid is one of the increasingly used critical infrastruc- ture that is essential for the functioning of a country. This coupled with Internet of Things (IoT) has huge potentials in several areas such as re- mote monitoring and managing of electricity distribution, traffic signs, traffic congestion, parking spaces, road warnings and even early detection of power influxes as a result of natural disasters, safety failures, equip- ment failures or carelessness. Despite the advantages of Smart Grids, there are security threats, privacy concerns and still open challenges re- lated to these issues in Smart Grids. This chapter seeks to provide a review of the security and privacy perspectives inherent in IoT enabled Smart Grids. Firstly the chapter explores the functionalities of Smart Grids as opposed to a traditional grid. Next the chapter provides an overview of Smart Grid architectures followed by positioning IoT con- cept into Smart Grid with a focus on architectures. Then, the proposed approach for identifying threats and attacks in IoT enabled Smart Grid, namely the security pyramid is presented. Lastly, we work on identifying the possible threats and attacks in the digital substation use case.
Numerous studies have shown that mobile devices like smartphones play a significant role in education these days, and it's clear that the influence and benefits of these devices regarding the potential for pedagogical perspectives are obvious. Mobile learning allows for flexibility by eliminating the need for learning to happen at a particular time and place. Moreover, mobile devices that can support Augmented Reality (AR) are becoming more powerful, less expensive and quite useful in the education process. In this paper, we conduct a study where mobile AR application alongside project-based learning is utilized in a classroom setting, where interviews are administered to understand the K-12 students' attitude towards the educational model based on mobile AR for enhancing the twenty-first century skills in chemistry education. The interviews are conducted with thirty students and their teacher who teaches them chemistry course. Four skills are investigated and discussed, i.e., innovation and creativity, critical skills and problem solving, communication and collaboration, and information culture. Based on the research findings, some recommendations are provided. Furthermore, in the scope of Agenda 2030 for Palestine, we believe these findings could serve as a basis for developing practical policy interventions attributed to enhancing the educational system with emerging technologies.
Nowadays, the growth of mobile apps is so fast and viral; they have the potential of transforming our everyday lives by creating huge opportunities to individuals and businesses. This translates into a growing demand for developing such apps, which need to be easy to learn and use. In this paper, we conduct an evaluation of an android mobile app, which we designed and developed to find and register power outlets in public spaces. Our evaluation of the prototype consisted of two stages. First, we provided the users with two tasks, with an additional option to indicate their perception of how easy it was to complete these tasks. Second, upon completing both tasks and offering their comments, participants were asked to take the SUS (System Usability Scores) questionnaire. The results of the evaluation indicate that the app usability and learnability is acceptable despite being a prototype. The findings and participants’ comments give us a direction on how this app can be improved in the future.
Nowadays, data has emerged as a resource of prime importance, and an insightful use of it in a data-centric workforce can provide continuous competitive advantage. In their journey of becoming data-driven, many organizations invest in underlying resources and adjust business models accordingly. In the academic literature, the importance of becoming data-driven and utilizing the data analytics tools in achieving the transformation vision has been widely discussed and explored. Nevertheless, when it comes to data democracy and opening the data within organizations, there is still a noticeable gap. The significance of this paper relies on the relevance of empowering organizations toward creating the right instruction on how to achieve data democracy in their data-driven transformation path. By focusing on a case study, this work reveals, among others, the correlation between the benefits from empowering different players of the organization with the needed data knowledge, and the acceleration of the digital transformation journey.
Research has demonstrated that software engineering teams nowadays face numerous challenges that revolve around the well-being (happiness) of the developers. One way to address these challenges can be through Mob Programming (MP). Mob programming represents a novel software development practice where a whole team works together on the same coding problem, at the same time, in the same space and on the same computer. In this study, we investigate the impact of using MP on the well-being of software developers. A qualitative method with semi-structured interviews and observations was applied for its ability to extract in-depth and valuable information. The participants selected for this study derived from four different development teams who worked at Fortnox AB in Växjö, where one of the four development teams used MP on a daily basis and the three other teams practise MP on an occasional basis. A total of 13 interviews were conducted at the company's offices. Thematic analysis is used to organize and create a structure regarding the information from the interviews. Two themes with specific sub-codes are created based on the thematic analysis: Team dynamics and Individual dynamics, which are derived from the interview questionnaire. The study found that the majority of software developers were impacted in a positive way regarding well-being while practising MP. Reduced stress and individual work pressure, and increased social interaction were two of some prominent factors that contributed to this result. However, stress was also found to have negative effects on some developers, which was associated with the constant attention and overlooking by others during the coding process
MOOCs are increasingly being considered by universities as an integral part of their curriculum. Nevertheless, there are several challenges that to some extent slow this process, where the most important one is the accreditation challenges and financing. These challenges are particularly important in the context of universities in Scandinavian countries where education is mostly free. In order to gain more insights on the status of proliferation of MOOCs in Scandinavian universities and understand any specific challenges, we conducted a study by analyzing two sources of data: research publications and university websites. Further on, these data have been analyzed using a framework that differentiates and categorizes MOOCs in terms of accreditation and scalability. As a result of this analysis, we have identified the remaining challenges as well as a number of opportunities regarding the full integration of MOOCs in the educational system of the Scandinavian Higher Education Institutions.
Providing researchers and other users access to data can accelerate knowledge discovery and enhance research transparency and reliability. In this context, the FAIR vision was formulated with the goal to optimize data sharing and reuse by humans and machines. In this paper, we investigate Scandinavian open data portals using FAIR data principles. We review and analyze the current state of datasets categorized as “Science and Technology” since in our view, such data is particularly relevant for reusability. Additionally, this study aims to further highlight and understand any specific challenges related to suitability of scientific data according to FAIR principles. Our findings highlight that, while the possibilities of open data from the science and technology domain are very promising, there are still a plethora of challenges we’ve discovered, and that need to be tackled in order to truly leverage the benefits.
Although many technological devices and solutions to enhance the skiing experience are now available for skiers, skiing sometimes could turn to be potentially dangerous. The speed of movement, environment unpredictability, and variable weather conditions, among others, can contribute to some of the most common skiing injuries that skiers incur. In this paper, we conduct an interface prototype evaluation of a ski injury registration system architecture that is already developed. This system will improve the communication from the ski resort to the medical center, in case an injury has occurred. The results of the interface evaluation indicate that the ski patrollers showed very positive attitude and experience with this prototype. Furthermore, the post-task and SUS (System Usability Scale) question results showed very high score for all participants, indicating that locating the body parts and the right injury was very easy using the interface.
Research has demonstrated that firms using e-business culminate with considerable returns through efficiency improvement, inventory reduction, sales increase,customer relationship enhancement, new market penetration, and ultimatelyfinancial returns. However, there is little systematic research in terms of e-businessadoption patterns in firms using TOE (Technology-Organization-Environment)framework. This paper illustrates the potential of adoption and use of ICT and ebusinessapplications in small and medium sized enterprises (SMEs) in Macedonia.In the paper we present preliminary results of a survey of around 60 SMEs. In thisstudy we explore several factors enabling or impeding the successful adoption anduse of e-business and ICT by Macedonian SMEs. Based on technologyenvironment-organization (TOE) theory, three aspects influence e-business adoption: technological context (we explore technology integration among firms,more specifically the type of ICT adoption and applications), organizational context(we try to discover the motivations to invest in ICT, the benefits and barriers of ICTand e-business in particular) and environmental context (here we investigate trusted sources of IT advice, challenges of implementations and competitive pressure). Wefind that SMEs are generally satisfied with their investment in ICT but they are concerned about the cost of such investments and are uncertain about the business benefits, failing to recognize ICT's strategic potential to increase business flexibility, to increase productivity and to support globalization. Besides the concern about the ICT related cost, other major obstacles in adopting ICT were lackof internal ICT capabilities and lack of information about selecting, implementing and evaluating suitable ICT solutions. Our findings have important implications for policy aimed at ICT adoption and use by SMEs and will provide a foundation forfuture research by helping policy makers to understand, assist and support the SME sector.
The high penetration of Internet, advances in mobile computing and the rise of smartphone usage has largely enhanced the use of social media in education. Moreover, nowadays social learning network (SLN) platforms have become an important educational technology component in higher education. Despite the fact that SLN are becoming ubiquitous in the higher education, there is relatively not much empirical work done investigating their purposefulness when integrated into the learning activities. This paper aims at exploring the impact of SLN in mobile assisted learning and to provide empirical evidence as to what extent SLN and mobile learning (M-learning) can improve the learning experiences. For this purpose, a quantitative experimental approach is used, and two survey questionnaires were conducted. The data is collected from 120 participants. In this study, we focus our intention on Edmodo and Kahoot platforms, which represent social media based tools that aid and support collaboration, knowledge sharing and group activities among students. Computer science students of the Tetovo University (TU) used these tools throughout one semester. From this study, there is significant evidence that students are very interested to use this SLN in a M-learning setting, indicating that SLN can be one of the promising pedagogical technologies that could contribute effectively to learning process.
Ontologies represent an immense opportunity and are bringing great advantages to e-learning systems. Their implementation is seen as a better solution for organizing and visualizing didactic knowledge, and for this knowledge to be shared and reused by different educational applications. This paper aims at proposing a model which is focused on integrating ontological principles with e-learning standards. We developed a prototype model that is integrated with an ontology which gives a semantic representation of learning contents by adding semantic notations to each learning resource. The ontology is used for identifying the structure of learning module and defining the needed vocabulary for the student to conceptualize the learning modules. Another special ontology is introduced for learning materials, which is located at the systems metadata. Here, we included also the system access options, results registering and the communications.
Massive Open Online Courses (MOOCs) are now the most recent topic within the field of e-learning. They have the potential to influence the higher education environments significantly worldwide by creating a completely new and large market of educational resources by overpassing the traditional universities market share due to their physical limitations. However, due to the many differences between mobile devices and desktop environments, the introduction of mobile technology in MOOC environment is challenging. Hence, the main objective of this paper is to study and compare the learner’s experience in different learning environments by using mobile devices and PCs while performing given tasks related to MOOCs. To achieve this goal, we conduct a subjective experiment with various MOOCs related tasks to be performed in mobile and desktop learning environment. The results of the findings show that the difficulties learners have experienced in the mobile environment are more expressed. Moreover, their satisfactory level is much higher in the desktop environment.
MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. Considering the differences between traditional learning paradigm and MOOCs, a new research agenda focusing on predicting and explaining dropout of students and low completion rates in MOOCs has emerged. However, due to different problem specifications and evaluation metrics, performing a comparative analysis of state-of-the-art machine learning architectures is a challenging task. In this paper, we provide an overview of the MOOC student dropout prediction phenomenon where machine learning techniques have been utilized. Furthermore, we highlight some solutions being used to tackle with dropout problem, provide an analysis about the challenges of prediction models, and propose some valuable insights and recommendations that might lead to developing useful and effective machine learning solutions to solve the MOOC dropout problem.
During the past two years, the entire world has been coping with the consequences of the COVID-19 pandemics. The need for physical distancing, forced an accelerated digital transformation of the education sector. The emergency remote education (ERE) has been manifested differently across diverse countries in the world. In this paper, we bring a case study about students’ and teachers’ impressions and experiences regarding the changes that have happened due to pandemic conditions in university courses in informatics at a Swedish university. This research is conducted through a mix of quantitative and qualitative empirical data. These data have been collected through the students surveys, course logs, as well as teachers and ICT pedagogue interviews. The collected data have been analyzed through the technology-mediated learning (TML) theoretical framework. Based on the thematic analysis on the collected data, we have identified three main themes: a) Preparedness, b) Challenges with ERE and c) Opportunities with ERE. As a result, through analyzing data in the light of the ERE experiences that encompasses the educational process, affordance, and beliefs, knowledge, and practices, we provide a set of lessons-learned experiences and indicate the possible lines of actions when it comes to the learning design in the constrained pandemic situations.
Recent advancements in big data, algorithms, and computing power have triggered significant enhancements in artificial intelligence (AI). Almost every aspect of travel and tourism is currently impacted by AI, which can be evidenced in a variety of applications including robots, conversational systems, smart travel agents, prediction and forecasting systems, voice recognition, and natural language processing. In this article, we examine how AI has altered and continues to alter the key operations and processes in the tourism industry, with special emphasis on sustainability. After applying the PRISMA framework to guide our search process, the study identified 69 relevant articles published between January 1, 2018, and November 1, 2022. The mapping results revealed that the field is expanding quickly, despite the noted obstacles and challenges. We identified several factors and challenges that should be considered in order to advance the level of research and development in this area. Among these factors, we emphasize the importance and the need for standardized and multimodal datasets, transformer-based and more advanced representation techniques, and standardized performance evaluation metrics for AI models. Also, based on these challenges, some recommendations are provided, and future research directions are identified.
The digital product passport has been introduced as a policy instrument to enable traceability throughout the product life cycle and to support a circular economy. Anyhow, as a relatively new concept, there are a lot of uncertainties throughout the industrial landscape regarding the challenges and opportunities it brings. One such branch of industry is the furniture sector. In this paper, we present a DPP solution for furniture industry based on blockchain technology and inspired by a design thinking mindset. Through a prototype implementation, we highlight the key aspects such as data governance, stakeholder constellation, data interoperability, and data integrity that have significant research potential. Furthermore, we discuss these key concepts of DPP through the lens of eco-design principles in order to promote sustainability, improve energy efficiency, and protect the environment.
The recent proliferation of Massive Open Online Courses (MOOCs) has initiated a plethora of research endeavors revolving around new pedagogical methods in higher education. Integrating MOOCs in blended learning can be beneficial in different ways for both learners and instructors. In this position paper, we aim to provide a brief and comprehensive review about the challenges that higher education institutions in Macedonia and Kosovo face while coping with the new trends of flexible or blended learning. Moreover, after describing some real cases of MOOC based flipped classroom learning, we also provide some recommendations in order to enhance and enrich learning experience by employing innovative pedagogies.
Artificial neural networks offer a unique way to model very complex and innovative systems that can be very effective in anticipating various accident severities. In this article, we propose a neural-network-based model, able to predict the number of severe injuries caused while skiing. The proposed system is intended for use by ski patrol and medical personnel to better prepare themselves in advance for treating ski-injured persons. The ski patrol and any other medical personnel will be able to know the statistics, type and severity of the injuries occurred, and most importantly, will be benefiting from having predictions for each day. Considering that, the number of injured people in a particular place each day was estimated, the results are very promising suggesting that such a system would prove beneficial in accurately predicting skiing injuries.
Abstract. While most enterprise data is unstructured and file based, the need for access to structured data is increasing. In order to reduce the cost for finding information and achieve relevant results there is a need to build a very complex query which indeed is a serious challenge. Data volumes are growing at 60% annually and up to 80% of this data in any organization can be unstructured. In this paper we focus on describing the evolution of some modern ontology-based information retrieval systems. Further, we will provide a brief overview of the key advances in the field of semantic information retrieval from heterogeneous information sources, and a description of where the state-of-the-art is at in the field. Finally, we present and propose a novel use of semantic retrieval model based on the vector space model for the exploitation of KB (Knowledge Base) to enhance and support searching over robust and heterogeneous environments.
The emergence and evolution of Internet of Things (IoT) offers great advantages to improve substantially the management over electricity consumption and distribution to the benefit of consumers, suppliers and grid operators. However, introducing IoT related devices and technologies in smart grids might lead to new security and privacy challenges. Though necessary technological innovations to ensure secure communication are being developed, more work is still required towards more secure standards for communication between devices and Smart Grids. This paper provides an overview about the security and privacy challenges of IoT applications in smart grids. Furthermore, we highlight and analyze some solutions and practices being used to cope with security and privacy requirements for IoT on deployment and management of smart grid. We address three types of challenge domains: customer domain, information and communication domain, and the grid domain.
Undoubtedly, MOOCs have the potential to introduce a new wave of technological innovation in learning. In spite of the great interest among the educators and the general public MOOCs have generated, there are some challenges that MOOCs might face when it comes to examining and determining the best pedagogical approaches that MOOCs should be based on. Moreover, MOOCs are facing also challenges towards building a consistent business model. The main objective of this paper is to shed more light on the MOOCs phenomenon, by analyzing and discussing some benefits and drawbacks of MOOCs from the pedagogical and business perspectives. Therefore, in this paper we provide an in-depth analysis of MOOCs challenges and opportunities towards determining pedagogical innovations. We also analyze current trends of MOOCs expansion to create new educational markets by overpassing the bricks-and-mortar educational institutions. To do so, we conduct a SWOT analysis on MOOCs. Finally, we provide possible directions and insights for future research to better understand how MOOCs can be improved to lead to greater innovations in the higher education landscape to answer the needs of a knowledge-based economy.
Skiing is a winter sport that is found very attractive to many people. Nevertheless, this sport is considered among high-risk sports due to the potential danger of severe injury or death. This is because of variable weather and terrain conditions, obstacles including other skiers, high speeds, trees, etc. Artificial Neural Networks have many applications in predicting the occurrence of various accident severities. In this article, we study the impact of the environmental factors to potential risk factor assessment in skiing. Hence, we apply the Bayesian Regularization Back Propagation neural network (BRBP) to predict the number of severe injuries in skiing, based on the data obtained from our prototype ski-injury registration system, the estimated bindings of environmental conditions, and the potential risk for resulting number of personal injuries. Through comparing with Levenberg Marquardt Back Propagation (LMBP), in terms of prediction accuracy, our experimental results show that BRBP has better performance by achieving higher predictive accuracy.
Heat demand prediction is an important part of increasing system efficiency within district heating. To achieve this efficiency, the energy provider companies need to estimate how much energy is re quired to satisfy the market demand. In this paper, we propose a method to investigate the application of online ma chine learning algorithm to achieve energy efficiency and optimization in District Heating (DH) systems by predicting the heat demand on the consumer side. To accomplish this, we are planning to use operational data from a Norwegian company (EffektivEnergi AS, Hamar) for a group of buildings that are connected to DH in other places.
We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.
Ontologies represent an efficient way of semantic web application on e-learning and offer great opportunity by bringing great advantages to e-learning systems. Nevertheless, despite the many advantages that we get from using ontologies, in terms of structuring the data, there are still many unresolved problems related to the difficulties about getting proper information about a learner’s behavior. Consequently, there is a need of developing tools that enable analysis of the learner’s interaction with the e-learning environment. In this paper, we propose a framework for the application of Business Intelligence (BI) and OLAP technologies in SMEUS e-learning environment. Hence, on one hand, the proposed framework will enable and support the decision-making by answering some questions related to learner’s performance, and on the other hand, will present a case study model for implementing these technologies into a semantic e-learning environment.
Nowadays, EU is supporting various projects to motivate and support citizen’s behavioral to achieve greater energy efficiency in district heating systems by taking advantage of ICT while ensuring energy savings from this new ICT-enabled solutions are greater than the cost for the provision of the services. Modern heating networks requires tools for constant monitoring and control of the network to ensure high effectiveness and reliability. Therefore, a flexible and efficient distributed data collection and storage, that operates with diverse sensors and via different types of networks is required. Moreover, such a system needs to be highly dependable, and separate parts of the system should operate even if the communication is lost. Cloud computing has an answer for this enormous network of computing resources and storage needs, since it has several good properties such as being energy saving, cost saving, agile, scalable, and flexible. In this paper, we propose a cloud computing based framework that integrates all the required advanced communication technologies to create an intelligent district heating network.
In recent years, sentiment analysis (SA) has gained popularity among researchers in various domains, including the education domain. Particularly, sentiment analysis can be applied to review the course comments in massive open online courses (MOOCs), which could enable instructors to easily evaluate their courses. This article is a systematic literature review on the use of sentiment analysis for evaluating students’ feedback in MOOCs, exploring works published between January 1, 2015, and March 4, 2021. To the best of our knowledge, this systematic review is the first of its kind. We have applied a stepwise PRISMA framework to guide our search process, by searching for studies in six electronic research databases (ACM, IEEE, ScienceDirect, Springer, Scopus, and Web of Science). Our review identified 40 relevant articles out of 440 that were initially found at the first stage. From the reviewed literature, we found that the research has revolved around six areas: MOOC content evaluation, feedback contradiction detection, SA effectiveness, SA through social network posts, understanding course performance and dropouts, and MOOC design model evaluation. In the end, some recommendations are provided and areas for future research directions are identified.
Nowadays, the impact of technology on education has been outstanding and this brings new challenges for educational institutions to manage many issues that were previously not managed with ease due to geographical limitations or lack of adequate training technologies. The extensive use of technology in learning makes its use inevitable in the assessment process. Although, a lot of software packages exist in the market, it is difficult to adapt them according to a certain educational system. In this paper we present the characteristics of the new model of e-testing system that we have designed and developed. This system is implemented and is used for testing by several schools of our country. Before applying the new system, students were tested in a classical way. We have gathered and compared the classical testing results and those from using e-testing tool for assessment of student knowledge. As a conclusion, the success of the students tested electronically is much higher comparing with the success of students tested classically.
The digitalization of society brings many opportunities and challenges, especially on how we organize the welfare society in the future. This becomes especially pertinent as we are heading toward a global increase of older people, which will strain healthcare and bring the challenge of building sustainable solu- tions. In this paper, we argue that the unsustainable solutions within healthcare are due to them being defined and ‘solved’ with a single approach or approaches used in silos. We advocate that a more sustainable solution could be achieved by combining systems thinking and design thinking perspectives throughout the entire process—from problem definition to solution offering. A benefit of such combined perspectives is the ability to develop a shared context among all stakeholders, which helps uncover unique tacit knowledge from their experience. This will serve as a solid foundation to generate unconventional ideas that will lead to sustainable and satisfactory solutions.
Open Data are increasingly being used for innovation, developing government strategies, and enhancing the transparency of the public sector. This data is aimed to be available to all people regardless of their abilities, professions and knowledge. Research is showing, however, that open data, besides being physically inaccessible to people with special needs, those are also semantically inaccessible to people who lack data science expertise. In order to identify specific accessibility challenges associated with open government data portals and datasets, we conducted an analysis using seven principles of Universal Design. In total, nine challenges are identified based on issues discovered. Three challenges are identified on the web portal interface level, namely: dataset filtering and categorization, access using a keyboard, and breadcrumb and back navigation. The other six challenges are identified on dataset level: dataset previewing, dataset size, dataset formats, dataset purpose, dataset labelling, and dataset literacy. For each challenge, we propose recommendations as a mean to incite a discussion about the features that open data should possess in order to be widely accessible, including people with disabilities and those lacking data science expertise and knowledge
Choosing the right method to sort numbers has a huge effect on how quickly a computer can process a task. The most used sorting algorithms today have been discovered years ago, and to this day, they have been the best for the job as there was no other competitive algorithm. Through this paper, we make an analysis and comparison between the state of the art algorithms in sorting and based on their analogy of functionality, we propose a new modified sorting algorithm. We then present a brief description of the new modified algorithm, conduct comparisons with the state of the art, and finally we give conclusions about the performance of the proposed algorithm versus the most popular sorting algorithms. Moreover, we highlight the benefits of using this algorithm in different fields by various business companies or software developers, in cases when they need faster and easier sorting for their data management.
Massive Open Online Courses (MOOCs) have transformed the way educational institutions deliver high-quality educational material to the onsite and distance learners across the globe. As a result, a new paradigm shifts as to how learners acquire and benefit from the wealth of knowledge provided by a MOOC at their doorstep nowadays in contrast to the brick and mortar settings is visible. Learners are therefore showing a profound interest in the MOOCs offered by top universities and industry giants. They have also attracted a vast number of students from far-flung areas of the world. The massive number of registered students in MOOCs, however, pose one major challenge, i.e., 'the dropouts'. Course planners and content providers are struggling to retain the registered students, which give rise to a new research agenda focusing on predicting and explaining student dropout and low completion rates in a MOOC. Machine learning techniques utilizing deep learning approaches can efficiently predict the potential dropouts and can raise an alert well before time. In this paper, we have focused our study on the application of feed-forward deep neural network architectures to address this problem. Our model achieves not only high accuracy, but also low false negative rate while predicting dropouts on the MOOC data. Moreover, we also provide an in-depth comparison of the proposed architectures concerning precision, recall, and F1 measure.
Many online learning websites and learning management systems (LMS) provide social collaboration and networking tools to aid learning and to interact with peers for knowledge sharing. The benefit of collaborating with each other is certainly undeniable, such tools, however, can be a distraction from the actual tasks for learners. The paper presents a study on social media tools supported by various eLearning systems to understand the impact on students learning activities. A survey questionnaire is designed for this purpose. The data is collected from students who have had experience using different massive open online course (MOOC) eLearning platforms and LMS from various universities. The results of the survey indicate that more than 95Â % of the participants use at least one of the social tools in their daily life activities, and almost 84Â % of them have used these tools in connection with the eLearning systems. It is also interesting to note that 92Â % of the participants intend to use social tools for study purposes. The results indicate that there is a need to integrate more of these social media tools into eLearning systems.
The prevention of crime is a multifaceted challenge with legal, political, and cultural implications. Surveillance technologies play a crucial role in assisting law enforcement and other relevant parties in this mission. Drones, cameras, and wiretaps are examples of such devices. As their use increases, it becomes essential to address related challenges involving various stakeholders and consider cultural, political, and legal aspects. The objective of this study was to analyze the impact of surveillance technologies and identify commonalities and differences in perspectives among social media users and researchers. Data extraction was performed from two platforms: Scopus (for academic research papers) and platform X (formerly known as Twitter). The dataset included 88,989 tweets and 4,874 research papers. Topic modeling, an unsupervised machine learning approach, was applied to analyze the content. The research results revealed that privacy received little attention across the datasets, indicating its relatively low prominence. The military applications and their usage have been documented in academic research articles as well as tweets. Based on the empirical evidence, it seems that contemporary surveillance technology may be accurately described as possessing a bi-directional nature, including both sousveillance and surveillance, which aligns with Deleuzian ideas on the Panopticon. The study’s findings also indicate that there was a greater level of interest in actual applications of surveillance technologies as opposed to more abstract concepts like ethics and privacy.
In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.
Analysing users’ behaviour and social activity for investigating suspects is an area of great interest nowadays, particularly investigating the activities of users on Online Social Networks (OSNs) for crimes. The criminal activity analysis provides a useful source of information for law enforcement and intelligence agencies across the globe. Current approaches dealing with the social criminal activity analysis mainly rely on the contextual analysis of data using only co-occurrence of terms appearing in a document to find the relationship between criminal activities in a network. In this paper, we propose a model for automated social network analysis in order to assist law enforcement and intelligence agencies to predict whether a user is a possible suspect or not. The model uses web crawlers suited to retrieve users’ data such as posts, feeds, comments, etc., and exploits them semantically and contextually using an ontology enhancement objective metric SEMCON. The output of the model is a probability value of a user being a suspect which is computed by finding the similarity between the terms obtained from the SEMCON and the concepts of criminal ontology. An experiment on analysing the public information of 20 Facebook users is conducted to evaluate the proposed model.
The introduction of MOOCs raised the expectation of a disrupting potential within education systems. These expectations, however, have not been met, despite the fact that more than a decade has passed. The main reason seems to be the high dropout rate of students in Massive Open Online Courses (MOOCs). With the start of the COVID-19 pandemic, the context of education systems has changed dramatically, as all were forced to shift to an online mode of education. In these new settings and leveraging the advancement of Artificial Intelligence (AI), in this research work, we report the findings by analyzing 90,294 reviews of MOOC completers in two subjects: Information Technology (IT) and Business. Using topic modeling with transformers, we have discovered the key themes that characterize MOOC completers’ reviews. The results show that for IT and Business courses, completers are not interested only in completing the course and earning a certificate, but are also interested in various aspects related to the course, instructor, and assessment. Furthermore, completers suggest that having short videos is more engaging and allows them to easily understand the content and have the ability for quick review. An interesting finding is that the completers of IT courses rated highly the organization and delivery of labs and hands-on sessions. Based on these findings, we propose some avenues for further development for MOOCs that consider the completers’ views and could potentially increase the retention rate in the MOOC environment.
Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail.
Since the beginning of the 21st century, the lifespan of people born with Down syndrome (DS) has increased. They now outlive their parents and rely on their relatives who usually sacrifice their own families to care for their disabled siblings. To reduce the pressure on families and the wider community, it is crucial to prepare DS people for independent life from early childhood. Emerging technologies can significantly support the process of acquiring the skills that are necessary for solving real-life problems at home and work. To assess their impact and estimate how much they are implemented in inclusive education, a review of 564 papers published after 2015 was done using the PRISMA review model. After gradual exclusion, 24 papers were used for the final review. Thematic analysis resulted in four themes with one common concept: variety. The results of examining the four research questions defined in the paper’s background confirm that the synergy of emerging assistive technologies and inclusive education has the potential of becoming a very effective strategy for creating an independent life for DS individuals. Many questions remain open, mainly related to a DS persons’ specific needs and capabilities. The acceptance of the proposed synergy will depend on them.
The rapid advances of technologies, constantly brings new demands for new skills and expertise of the professionals in IT industry. There is a constant need for people that have in-depth understanding and know how to develop the new innovative services using these new technologies. In these settings, the real challenge is how to find the right persons with the right education in an industry where the in-thing yesterday may be out-of-date tomorrow? To add to this challenge, universities are still “increasingly stove-piped in highly specialized disciplinary fields” (Hurlburt et al., 2010) as well as there is a lack of flexibility for the professionals to have their competences developed. All this points out the great challenges that universities are facing for alignment between academic development within degree curricula and the requirements that industry demands for their specific needs (Falcone et al. 2014). In this research effort we report our experiences from an ongoing Graduate Professional Development Program where we address these challenges through a co-creation process with IT industry based on open innovation. Through this model we bring together research expertise, academic experience and experts from industry in a collaborative process for developing courses to suit the current needs of IT professionals. As an outcome of this process, the course content is tailor-made, as well as everything else in connection, such as: bite-size modules, adjustable pace, open and online educational resources, as well as a flipped classroom approach to teaching. As a result, we have developed and provided so far five courses that have been very well accepted by the IT professional. Thus, in this paper we aim to provide some insights on approaches for facilitating continuous competence development plans for IT professionals within regular university educational offer.
Nowadays, Artificial Intelligence (AI) is proving to be successful for solving complex problems in various application domains. However, despite the numerous success stories of AI-systems, one challenge that characterizes these systems is that they often lack transparency in terms of understandability and explainability. In this study, we propose to address this challenge from the design thinking lens as a way to amplify human understanding of ML (Machine Learning) and AI algorithms. We exemplify our proposed approach by depicting a case based on a conventional ML algorithm applied on sentiment analysis of students’ feedback. This paper aims to contribute to the overall discourse of a need of innovation when it comes to the understandability and explainability of ML and AI solutions, especially since innovation is an inherent feature of design thinking.
Improving and strengthening cybersecurity in the public sector should represent a top priority for government agencies, including municipalities and regions. To be resilient against cyberattack surges, organizations should consider establishing a cybersecurity program based on international standards and best practices. In this paper we explore the cybersecurity compliance in the Swedish public sector in relation to the best practices and guidelines highlighted in the ISO/IEC 27001A framework. Our findings indicate that the overall security status among the municipalities and regions contained many flaws, with substantial holes and critical issues. ISO/IEC 27001A creates a standardized base, but it is somewhat theoretical and starts with a policy, not providing insights on how to govern information security. Also, most of these “ISO/IEC”-related gaps were found to have been compiled into a single “Technology” domain. Though compliance with standards, best practices, and regulatory requirements can help reduce cyber risks, it does not guarantee that an organization will have strong cybersecurity. To address this issue and assess how well organizations can protect, discern, react, and recover from cyberattacks, an effective method for measuring security performance must be developed.
Non-fungible token (NFT) trade has grown drastically over recent years. While scholarship on the technical aspects and potential applications of NFTs has been steadily increasing, less attention has been directed to the human perception of or attitudes toward this new type of digital asset. The aim of this research is to investigate what concerns are expressed in relation to non-fungible tokens by those who engage with NFTs on the social media platform Twitter. In this study, data was gathered through online social media data mining of NFT-related posts on Twitter. Two datasets (with 18,373 and 36,354 individual tweet records, respectively) were obtained. Topic modeling was used as a method of data analysis. Our results reveal 19 overall themes of concerns around NFTs as expressed on Twitter, which broadly fall into two categories: concerns about attacks and threats by third parties; and concerns about trading and the role of marketplaces. Overall, this study offers a better understanding of the expressions of concern, uncertainty, and the perception of possible barriers related to NFT trading. These findings contribute to theoretical insight and can, moreover, function as a basis for developing practical design and policy interventions.
This paper presents results of a subjective experiment of user behaviour analysis on state-of-the-art learning management systems (LMS) and massive open online courses (MOOCs). The purpose of this study is to conduct a usability analysis on different eLearning platforms by observing subjects facial expressions, and based on the generated results to speculate which of the platforms are easy to use and work with for a new user. An experiment is designed for this purpose with different tasks that each subject has to perform, while they are being recorded. The facial recordings are analysed to find seven emotional engagement attributes and three sentiment engagement attributes using facial expression software. The results of our work show some very interesting findings. Additionally we have also proposed some recommendations based on an extensive comparison of features among different LMS that will provide better content personalization and customization, thereby improving learning outcome.
In the past few years, there has been significant progress made in the area of blockchain. The use of blockchain technology has the potential to revolutionize the educational system by providing individuals with innovative and cost-effective ways to learn, as well as by altering the way teachers and students work together. Additionally, blockchain technology can be utilized for the issuing of unchangeable digital certificates, and it can enhance the present limitations of the existing certificate verification systems by making them quicker, more reliable, and independent of the central authority. The application of blockchain in the context of education has generated significant scientific interest in this field. Nonetheless, research endeavors on the adoption of blockchain in the verification of academic credentials are still in the development phase. In order to shed more light on the field, in this paper we focus on extensively reviewing the body of knowledge on blockchain-based systems for academic certificate verification. Hence, the purpose of this survey is to compile all relevant research into a systematic literature review, highlighting the key contributions from various researchers throughout the years with a focus on the past, present, and future. In this work, we have identified 34 relevant studies out of 1744 papers that were published between 2018 and 2022 by employing the PRISMA framework. We distinguished six major themes covered by the research articles analyzed and also identified research gaps that need to be addressed and explored by the research community. Based on the findings of this review, we provide some recommendations for future research directions and practical applications that can assist researchers, policymakers, and practitioners in the field.
Nowadays, the centralized systems used in higher education institutions are sophisticated and have high security mechanisms, offering secure data transfer and real-time encryption. Despite the prevalent usage of centralized systems in many institutions, particularly those within the realm of higher education, some unresolved concerns persist pertaining to privacy, potential abuse, transparency, and the limited capacity to digitize numerous services. Blockchain systems are considered as a potential solution for addressing these constraints. This study begins by highlighting the significance of implementing blockchain systems in higher education institutions, while also outlining the obstacles encountered by researchers in this domain. Centralized systems and blockchain systems are distinguished, with a description of the challenges related to data transfer and adaptation to different platforms. A thorough explanation of the proposed blockchain system begins with a presentation of the conceptual model, followed by a detailed architecture of the processes that would be executed by the system, with particular emphasis on the generation and authentication of academic credentials. Additionally, an analysis is provided on the significance of smart contracts in the programming of blockchain systems. This includes a detailed explanation of the main smart contract architecture used in the proposed blockchain system. This article aims to explore the development of a proposed blockchain system and its practical implementation for testing purposes using specific scenarios and data in the foreseeable future.
The integration of Artificial Intelligence (AI) and blockchain technology (BT) into diploma generation and verification systems enables the digitalization of services in higher education institutions (HEI). These technologies have the potential to prevent misuse, protect identity and privacy, decentralize services, and automate processes. AI employs advanced pattern recognition and anomaly detection algorithms to ensure the integrity of academic certificates stored on the blockchain. By using smart contracts on the blockchain, artificial intelligence algorithms may automate and optimize the verification process, thereby diminishing the administrative load linked to validating academic qualifications. This review paper provides insights into how AI-driven algorithms can streamline the generation of digital diplomas, enhance authentication mechanisms, and mitigate fraudulent activities. Additionally, it highlights the potential of AI-powered analytics for optimizing blockchain-based systems, facilitating seamless interoperability, and ensuring trustworthiness in academic credential verification.
The implementation of a blockchain system for the automatic generation of academic credentials in a higher education institution can be considered as a safeguard against potential instances of abuse and counterfeiting of diplomas. However, the utilization of blockchain technology in the field of education, particularly within higher education institutions, appears to be limited in scope. This article aims to elucidate the advantages and constraints associated with the use of blockchain technology in higher education organizations. In addition, we present the ideas towards developing a blockchain-based prototype model that targets the automatic generation of diplomas using smart contracts and ensures the fast and secure verification of diplomas. In order to provide a more comprehensive understanding of the blockchain system, we aim to expand on its description via the use of graphical representations in the form of use case diagrams for each actor involved in the system. Additionally, we provide use case scenarios, as well as outline the functional as well as the nonfunctional requirements that are essential for the proper functioning of such a system.
Many higher education institutions in the world have switched to online learning due to the ongoing COVID-19 pandemic, which also has greatly contributed towards an increase of MOOCs enrollments across various disciplines. There are many factors that can influence the learning trajectory in MOOCs settings, and in order to gain a deeper understanding of learners' experience, we employ a quantitative research method, in which sentiment analysis and topic modeling are applied. In this perspective, learners' reviews from the learning platform Coursera are examined to identify the main topics associated with the course and the learners' attitudes and opinions towards these topics. For this purpose, a total of 28,281 reviews scraped from five courses within the field of data science are analyzed, and consequently nine course topics for which learners have commented on are found. The identified topics include: content, delivery, assessment, learning experience, tools, video material, teaching style, instructor skills and course provider. Next, each topic is assigned a sentiment score using a lexicon-based approach, and the topics which mostly affect the learning experience are finally determined and discussed.
In this paper, we present the growth barometer (Vekstbarometer in Norwegian), which is a digital platform that provides the development trends in the regional context in a visual and user-friendly way. The platform is developed to use open data from different sources that is presented mainly in five main groups: goals, premises or prerequisites for growth, industries, growth, and expectations. Furthermore, it also helps to improve decision-making and transparency, as well as provide new knowledge for research and society. The platform uses sensitive and non-sensitive open data. In contrast to other similar digital platforms from Norway, where the data is presented as raw data or with basic level of presentations, our platform is advantageous since it provides a range of options for visualization that makes the statistics more comprehensive.