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Data-Driven Solutions Enhancing Adaptive Education Through Technological Innovations for Disability Support
Abstract
This chapter explores the transformative role of data-driven solutions in advancing adaptive education for students with disabilities. This chapter examines how advanced data analysis techniques enable the creation of personalized learning environments tailored to individual needs and challenges. Key topics include innovative data collection methods, such as learning analytics and behavioral data, and their application in designing customized educational tools and strategies. The chapter also presents real-world case studies showcasing the successful use of data-driven approaches to enhance accessibility and inclusivity in educational settings. By providing a comprehensive analysis, this chapter underscores the potential of data-driven solutions to improve educational outcomes, foster equity, and create more effective support systems for students with disabilities.
... Data analytics facilitates the creation of customized educational instruments that specifically target unique learning obstacles, particularly for learners with disabilities (Nagarajan et al., 2024). Methodologies such as learning analytics and the collection of behavioural data support the formulation of individualized strategies that promote accessibility and inclusivity within the educational sphere (Nagarajan et al., 2024). ...
... Data analytics facilitates the creation of customized educational instruments that specifically target unique learning obstacles, particularly for learners with disabilities (Nagarajan et al., 2024). Methodologies such as learning analytics and the collection of behavioural data support the formulation of individualized strategies that promote accessibility and inclusivity within the educational sphere (Nagarajan et al., 2024). ...
This study aims to analyze the implementation of data-based management in improving the effectiveness of education in AI-assisted learning at the high school level. The analysis units in this study are data management systems and AI learning strategies in schools. The research design used is a qualitative study with a descriptive approach. The main data sources consist of principals, teachers, and high school students involved in the use of AI in learning. Data collection techniques include in-depth interviews, observations, and document analysis. The data is analyzed using thematic analysis techniques to identify patterns in the implementation of data-based management. The results show that the optimal use of data-based management can increase the effectiveness of AI-assisted learning through adaptive and evidence-based strategies. The main contribution of this research is to provide insights into how systematic data management can support technology-based educational innovation in the school environment.
The pursuit of sustainability has become a central focus in global investment strategies, as businesses, governments, and financial institutions seek to address environmental, social, and governance (ESG) challenges. Financial innovation plays a pivotal role in facilitating sustainable practices by integrating cutting-edge technologies such as Artificial Intelligence (AI), Extended Reality (XR), and data analytics into investment decision-making processes. This chapter explores the transformative potential of these technologies in reshaping the landscape of sustainable finance. It discusses how AI enables predictive analytics, risk assessment, and optimization of investment portfolios aligned with sustainability goals, while XR offers immersive tools for visualizing and simulating environmental and social impacts, aiding investors in making more informed decisions. Additionally, data-driven insights are emphasized as key enablers for measuring and tracking the success of sustainable investments. Through a combination of case studies and practical applications, this chapter illustrates how financial innovation, when combined with AI, XR, and data, can create impactful investment opportunities that contribute to a resilient and sustainable future. The chapter also highlights challenges, such as data privacy concerns and the need for standardized frameworks, and offers recommendations for harnessing these technologies effectively in the pursuit of sustainable finance.
Data engineering plays a pivotal role in advancing sustainability efforts by providing the tools and frameworks necessary to collect, process, and analyze vast amounts of environmental data. This chapter explores the essential role of data engineering in building resilient, sustainable systems across various sectors, including energy, agriculture, water management, and waste reduction. By focusing on the core principles of data engineering, including data collection, storage, integration, and analysis, this chapter highlights how these practices are fundamental to driving informed decision-making for sustainable development. It also delves into the challenges faced in integrating disparate data sources, ensuring data quality, and implementing scalable solutions that can adapt to the evolving demands of sustainability. Through real-world case studies, the chapter illustrates how effective data engineering can optimize resource management, improve environmental monitoring, and support the transition to a low-carbon economy. Furthermore, the chapter emphasizes the importance of cross-disciplinary collaboration and innovation in leveraging data engineering to address the pressing environmental challenges of today and tomorrow. As the world moves towards a more sustainable future, data engineering will continue to be at the forefront of enabling systems that are not only efficient but also resilient to the complexities of a changing environment.
The integration of project management with supply chain and big data engineering using AI methodologies has emerged as a strategic approach to enhance operational efficiency, decision-making, and sustainability. This chapter explores the intersection of these domains, highlighting how AI-driven insights and automation can streamline supply chain processes, improve data accuracy, and optimize resource allocation. AI methodologies such as machine learning, predictive analytics, and natural language processing enable real-time monitoring, demand forecasting, and risk mitigation, leading to improved supply chain resilience and reduced environmental impact. By embedding AI into supply chain management and project execution, organizations can achieve greater agility, cost savings, and sustainability, ensuring a competitive edge in a rapidly evolving market landscape. The chapter also discusses challenges and future opportunities in integrating AI-driven project management and supply chain engineering for long-term sustainable growth.
Automated Device Management (ADM) and Multi-Tenant Architecture (MTA) are emerging as crucial enablers of scalable, efficient, and sustainable technology solutions. This chapter explores the integration of ADM with MTA to enhance resource optimization, energy efficiency, and cost-effectiveness in educational and sustainability-driven applications. By leveraging cloud-based automation, AI-driven monitoring, and IoT-enabled devices, institutions can achieve seamless provisioning, real-time analytics, and adaptive learning environments. The study highlights key challenges, including security, interoperability, and data privacy, while proposing innovative strategies for overcoming these barriers. A case study on educational technology adoption demonstrates the impact of ADM and MTA in creating inclusive, accessible, and sustainable digital learning ecosystems. The findings emphasize the role of automation and multi-tenancy in fostering a scalable, resilient infrastructure for education and sustainability.
The integration of Artificial Intelligence (AI) and cybersecurity in automated eligibility and enrollment workflows is transforming the way organizations manage and streamline their operations. As industries, particularly healthcare and government sectors, face the growing challenge of securing sensitive data while ensuring efficient service delivery, AI-powered systems offer solutions that enhance both security and operational efficiency. This chapter explores the convergence of AI and cybersecurity in automating eligibility and enrollment processes, emphasizing how AI can improve decision-making, reduce manual errors, and ensure compliance with data privacy regulations. It also examines the role of cybersecurity in safeguarding personal and sensitive information throughout the automation process, addressing key concerns such as data breaches, identity theft, and fraud prevention.
This chapter explores the integration of AI-powered dashboards into state government programs to enhance real-time decision-making capabilities. With the increasing complexity of public sector operations and the need for data-driven insights, AI-powered dashboards offer a transformative solution for government agencies. These dashboards utilize advanced machine learning algorithms and data visualization techniques to provide actionable insights, predictive analytics, and dynamic reporting. By embedding AI capabilities, state governments can optimize resource allocation, improve policy implementation, and respond proactively to emerging challenges.
Cloud computing has emerged as a transformative technology in the education sector, offering scalable, cost-effective, and sustainable solutions to address the evolving needs of learners and institutions. This chapter explores how cloud computing enables seamless access to educational resources, enhances collaboration, and supports personalized learning experiences. It delves into the integration of cloud-based tools for virtual classrooms, learning management systems, and data analytics, emphasizing their role in fostering inclusivity and accessibility. Furthermore, the chapter highlights the environmental benefits of cloud adoption, including reduced energy consumption and carbon footprint, aligning with global sustainability goals.
This chapter explores the transformative role of data science in advancing sustainable supply chain management. By leveraging cutting-edge data analytics, machine learning, and artificial intelligence, businesses can optimize their supply chain operations, reduce environmental impact, and enhance resource efficiency. The chapter examines key applications of data science in various stages of the supply chain, including demand forecasting, inventory management, logistics optimization, and supplier selection. Additionally, it highlights the integration of sustainability metrics and circular economy principles to drive eco-friendly practices within supply chains. Through case studies and industry examples, the chapter demonstrates how data-driven decision-making can lead to more sustainable, cost-effective, and resilient supply chains, contributing to both business success and environmental stewardship.
The integration of Artificial Intelligence (AI) and Large Language Models (LLMs) is revolutionizing the landscape of healthcare and life sciences, offering innovative solutions for diagnostics, treatment, drug discovery, personalized medicine, and clinical decision-making. This paper explores the multifaceted applications of LLMs and AI technologies, including their roles in automating clinical documentation, enhancing patient engagement through intelligent virtual assistants, predicting disease progression, and supporting genomic research. We also delve into case studies that illustrate the real-world impact of AI-driven solutions in clinical workflows and pharmaceutical research. Moreover, the paper discusses the challenges related to data privacy, model interpretability, regulatory compliance, and ethical considerations. By examining current capabilities and emerging trends, this study aims to highlight how LLMs and AI are poised to shape the future of healthcare and life sciences with greater efficiency, precision, and accessibility.
The rapid advancement of Large Language Models (LLMs) has the potential to revolutionize vocational education by enhancing learning experiences, improving accessibility, and providing personalized instruction at scale. This chapter explores how LLM innovations are transforming vocational education, particularly in the context of skill development, training, and certification processes. By leveraging LLMs, vocational education programs can offer adaptive learning platforms, real-time feedback, and intelligent tutoring systems that cater to diverse learner needs. The chapter delves into the integration of LLMs in curriculum design, assessment, and career guidance, highlighting the role of AI-driven tools in bridging the gap between traditional educational models and the evolving demands of the workforce.
In the era of digital transformation, optimizing enterprise application deployment is critical for efficiency, scalability, and sustainability. This Chapter explores strategies for effective package distribution and installation, focusing on reducing resource consumption, minimizing environmental impact, and enhancing deployment efficiency. It highlights the role of automation, cloud-based deployment models, and containerization in streamlining software distribution while ensuring reliability and security. Furthermore, the study examines the significance of sustainable deployment practices, such as energy-efficient computing, reducing software bloat, and leveraging open-source tools. Additionally, the paper emphasizes the educational aspect, advocating for the integration of sustainable software deployment methodologies in technical curricula to equip future professionals with eco-conscious development skills. By adopting optimized deployment strategies, enterprises can achieve operational excellence while contributing to global sustainability goals.
The rapid evolution of software engineering and big data management has significantly influenced vocational education, equipping learners with industry-relevant skills. This chapter explores emerging trends in software project execution, emphasizing agile methodologies, DevOps practices, and AI-driven automation. Additionally, it delves into the role of big data in decision-making, cloud computing, and scalable data architectures tailored for vocational training. The integration of these technologies enhances hands-on learning, fosters innovation, and bridges the skill gap between academia and industry. By examining case studies and best practices, this chapter provides a roadmap for effectively incorporating modern software engineering principles and big data strategies into vocational education.
Sustainable supply chain practices have gained significant attention in recent years, driven by the growing need for businesses to reduce their environmental impact while maintaining operational efficiency. This chapter introduces a teaching framework that integrates data science methodologies to enhance sustainability within supply chains. By leveraging machine learning, predictive analytics, and optimization techniques, the framework enables the identification of key sustainability metrics and the development of data-driven strategies to optimize resource utilization, reduce waste, and minimize carbon footprints
Artificial intelligence (AI) has ushered in transformative changes, championing inclusion and accessibility for individuals with disabilities. This article delves into the remarkable AI-driven solutions that have revolutionized their lives across various domains. From assistive technologies such as voice recognition and AI-powered smart glasses catering to diverse needs, to healthcare benefiting from early disease detection algorithms and wearable devices that monitor vital signs and alert caregivers in emergencies, AI has steered in significant enhancements. Moreover, AI-driven prosthetics and exoskeletons have substantially improved mobility for those with limb impairments. The realm of education has not been left untouched, with AI tools creating inclusive learning environments that adapt to individual learning styles, paving the way for academic success among students with disabilities. However, the boundless potential of AI also presents ethical concerns and challenges. Issues like safeguarding data privacy, mitigating algorithmic bias, and bridging the digital divide must be thoughtfully addressed to fully harness AI’s potential in empowering individuals with disabilities. To complement these achievements, a robust conceptual model for AI disability inclusion serves as the theoretical framework, guiding the development of tailored AI solutions. By striking a harmonious balance between innovation and ethics, AI has the power to significantly enhance the overall quality of life for individuals with disabilities across a spectrum of vital areas.
This research paper explores the potential impact of artificial intelligence (AI) on higher education. The integration of AI has the potential to revolutionize teaching and learning, optimize administrative processes, and enhance research capabilities. The use of adaptive learning systems, intelligent tutoring systems, and virtual learning assistants, powered by AI, can help personalize learning, provide real-time feedback and support, and foster increased student engagement. Moreover, AI-powered administrative systems can automate routine tasks such as admissions, enrollment, and financial aid processing, freeing up valuable resources for more strategic initiatives. The application of predictive analytics, empowered by AI, can enable universities to harness the power of large datasets to make data-driven decisions. However, ethical considerations, potential biases, and concerns about faculty roles and pedagogical implications must also be taken into account. Ensuring fairness, transparency, and accountability in AI algorithms and protecting student data privacy and security are critical. The impact of AI on faculty roles and job displacement is another pressing concern, which may require proactive measures to support faculty. The paper concludes that responsible and ethical implementation of AI technologies in higher education requires thorough scrutiny of its complex facets, including the balance between human-driven and AI-driven instruction and potential reliance on AI in decision-making processes, to ensure that AI is used to its full potential for the benefit of students, faculty, and society.
The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.
Reflecting on twenty years of educational research, we retrieved over 400 research article on the application of artificial intelligence (AI) and deep learning (DL) techniques in teaching and learning. A computerised content analysis was conducted to examine how AI and DL research themes have evolved in major educational journals. By doing so, we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade, due to the discipline’s dynamism. By examining the major research themes and historical trends from 2000 to 2019, we demonstrate that, as advanced technologies in education evolve over time, some areas of research topics seem have stood the test of time, while some others have experienced peaks and valleys. More importantly, our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational research. For instance, the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning analytics. Furthermore, this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.
The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to investigate London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to the different areas of sustainability. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit. process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to a wide variety of practical uses and applications in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, and Barcelona has the best practices in the data-oriented competences, notably horizontal information platform, operations center, dashboards, training programs and educational institutes, innovation lab, research center, and strategic planning office. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city as to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism as a set of practices and strategies for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism.
This chapter investigates the synergistic potential of integrating artificial intelligence (AI) and quantum technologies to foster sustainable practices within supply chain management. With the growing emphasis on environmental responsibility and ethical sourcing, organizations are seeking innovative solutions to optimize their supply chains while minimizing environmental impact. By leveraging AI's predictive capabilities and quantum computing's computational power, this chapter explores how organizations can enhance decision-making processes, optimize resource utilization and promote sustainability across their supply chains. Through case studies and practical examples, the chapter demonstrates how the integration of AI and quantum technologies can enable real-time monitoring, predictive analytics, and adaptive optimization strategies, ultimately contributing to a more sustainable and resilient supply chain ecosystem.
This chapter explores the paradigm shift in supply chain logistics driven by the convergence of quantum computing and data-driven decision making. With the exponential growth of data and the increasing complexity of supply chain networks, traditional decision-making approaches are becoming inadequate in meeting the demands of modern logistics operations. By integrating quantum-inspired methodologies with data analytics, organizations can unlock new insights and optimize their decision-making processes across the supply chain. This chapter examines the principles of quantum computing and how they can be applied to enhance decision making in areas such as demand forecasting, inventory management, transportation optimization, and risk analysis. Through case studies and practical examples, the chapter illustrates the transformative impact of adopting a quantum perspective on data-driven decision making in supply chain logistics.
This chapter explores the transformative potential of integrating artificial intelligence (AI) with quantum computing to revolutionize inventory management practices. In today's dynamic business environment, efficient inventory management is paramount for organizations seeking to meet customer demand while minimizing costs and maximizing profitability. By harnessing AI's cognitive capabilities and quantum computing's computational prowess, this chapter elucidates how organizations can achieve unprecedented levels of optimization in their inventory operations. From demand forecasting and order fulfillment to supply chain optimization and risk mitigation, AI-driven quantum computing empowers organizations to make data-driven decisions with unparalleled accuracy and agility. Through real-world examples and case studies, this chapter showcases the tangible benefits of leveraging AI and quantum computing in inventory management, paving the way for enhanced operational efficiency, improved customer satisfaction, and sustainable competitive advantage in the marketplace.
This chapter explores the application of quantum machine learning (QML) techniques for demand prediction in supply chain networks. Traditional demand forecasting methods often struggle to capture the intricate dynamics and uncertainties present in modern supply chains. By leveraging the computational power and probabilistic nature of quantum computing, coupled with the flexibility and adaptability of machine learning algorithms, organizations can enhance the accuracy and efficiency of their demand prediction processes. This chapter provides an overview of QML methodologies tailored specifically for demand prediction in supply chain networks, highlighting their advantages over classical approaches. Through case studies and practical examples, the chapter demonstrates how QML can enable organizations to make more informed decisions, optimize inventory levels, and improve overall supply chain performance.
This chapter explores the transformative impact of artificial intelligence (AI) integration in air traffic management, offering insights into the paradigm shift witnessed in aviation practices. The study delves into the dynamic landscape of aviation, focusing on the pivotal role of AI in enhancing safety, efficiency, and overall operations within air traffic management systems. Notably, the results stemming from AI implementations showcase promising quantitative outcomes. These include a 15% reduction in average flight delays and a substantial 20% increase in airspace capacity utilization following the introduction of AI-driven traffic flow management. Moreover, a remarkable 30% decrease in reported near-misses and a 25% reduction in aviation accidents reflect the tangible improvements in safety measures derived from AI technologies. AI-enabled route optimization strategies demonstrated a 12% decrease in fuel consumption and a 10% reduction in flight duration for long-haul flights, while yielding $100 million in annual cost savings for the aviation industry.
Agile methodologies have revolutionized project management by emphasizing flexibility, collaboration, and responsiveness. However, effective implementation of Agile requires more than just adopting new processes—it demands agile leadership. This chapter explores the critical role of leadership in navigating the challenges of Agile implementation and maximizing its success. It examines the characteristics of agile leadership, including fostering a culture of trust, empowering teams, and embracing change. Drawing from both theory and real-world examples, the chapter provides practical insights into how leaders can inspire innovation, drive alignment, and sustain momentum in Agile environments. From managing resistance to facilitating continuous improvement, agile leadership is essential for unlocking the full potential of Agile methodologies. By equipping leaders with the skills and mindset needed to navigate complexity and uncertainty, organizations can achieve greater agility, resilience, and ultimately, success in today's fast-paced world.
The Research explores exponential intelligence and its significant influence on reshaping businesses. It delves into how cutting-edge technologies like artificial intelligence, blockchain, the Internet of Things, and data analytics can revolutionize traditional business models, sparking exponential growth. It highlights these technologies' applications across diverse industries and addresses the challenges and opportunities of embracing exponential intelligence. Moreover, it offers strategies for organizations to navigate this transformative journey. By leveraging exponential intelligence, businesses can discover fresh paths for innovation, improve operational effectiveness, and secure a competitive advantage in the era of digital evolution.
Mapping vegetation using remotely sensed images involves considering various factors, procedures, and strategies. The accessibility of remote sensing images has greatly increased due to advancements in remote sensing technology. Different types of images, characterized by their spectral, spatial, radiometric, and temporal properties, are suitable for different vegetation mapping purposes. To identify and map vegetation cover from remotely sensed images, it is often necessary to first create a vegetation classification system, either at the community or at the species level. Following this, the goal is to establish connections between the identified vegetation categories (communities or species) within this classification system and the distinguishable spectral characteristics present in remote sensing data. This spectral classification is eventually transformed into the types of plants during the image interpretation process, also known as image processing. This book chapter provides an overview of the process of classifying and mapping plant cover using remote sensing data. Additionally, it includes a case study at the end of the chapter, which can be highly valuable for researchers working in the same field.
Special education for students is essential to gain/get/obtain equity and accessibility. AI enables new tools, customized learning, and improved accessibility for students with different learning needs. This chapter examines AI in inclusive education and its benefits for special needs pupils. AI-based adaptive learning, assistive intelligent tutoring, and early intervention data analytics boost inclusive education. AI algorithms customize learning in adaptive learning systems. AI-powered voice recognition and accessibility technologies assist impaired students' study. AI-powered tutoring methods improve student performance. AI-powered data analytics can identify struggling students and give assistance. AI in inclusive education has downsides. The chapter addresses AI bias, fairness, data privacy and security, transparency, explainability, and stakeholder participation. Ethics should govern inclusive education's usage of AI technology. AI should support human judgement and interaction. Responsible AI can improve education for all children, regardless of learning needs.
This is the first systematic literature review concerning the interconnections between big data (BD) and co-innovation. It uses BD as a common perspective of analysis as well as a concept aggregating different research streams (open innovation, co-creation and collaborative innovation). The review is based on the results of a bibliographic coupling analysis performed with 51 peer-reviewed papers published before the end of 2019. Three thematic clusters were discovered, which respectively focused on BD as a knowledge creation enabler within co-innovation contexts, BD as a driver of co-innovation processes based on customer engagement, and the impact of BD on co-innovation within service ecosystems. The paper theoretically argues that the use of BD, in addition to enhancing intentional and direct collaborative innovation processes, allows the development of passive and unintentional co-innovation that can be implemented through indirect relationships between the collaborative actors. This study also makes eleven unique research propositions concerning further theoretical developments and managerial implementations in the field of BD-driven co-innovation.
This book shares original innovations, research, and lessons learned regarding teaching and technological perspectives on trust-based learning systems. Both perspectives are crucial to enhancing the e-Assessment process.
In the course of the book, diverse areas of the computer sciences (machine learning, biometric recognition, cloud computing, and learning analytics, amongst others) are addressed. In addition, current trends, privacy, ethical issues, technological solutions, and adaptive educational models are described to provide readers with a global view on the state of the art, the latest challenges, and potential solutions in e-Assessment. As such, the book offers a valuable reference guide for industry, educational institutions, researchers, developers, and practitioners seeking to promote e-Assessment processes.
Purpose
The purpose of this paper is to identify current technologies related to Industry 4.0 and to develop a rationale to enhance the understanding of their functions within a data-driven paradigm.
Design/methodology/approach
A systematic literature review of 119 papers published in journals included in the Journal Citation Report (JCR) was conducted to identify Industry 4.0 technologies. A descriptive analysis characterizes the corpus, and a content analysis identifies the technologies.
Findings
The content analysis identified 111 technologies. These technologies perform four functions related to data: data generation and capture, data transmission, data conditioning, storage and processing and data application. The first three groups consist of enabling technologies and the fourth group of value-creating technologies. Results show that Industry 4.0 publications focus on enabling technologies that transmit and process data. Value-creating technologies, which apply data in order to develop new solutions, are still rare in the literature.
Research limitations/implications
The proposed framework serves as a structure for analysing the focus of publications over time, and enables the classification of new technologies as the paradigm evolves.
Practical implications
Because the technical side of the new production paradigm is complex and represents an evolving field, managers benefit from a simplified and data-driven approach. The proposed framework suggests that Industry 4.0 should be approached by looking at how data can create value and at what role each technology plays in this task.
Originality/value
The study makes a direct link between Industry 4.0 technologies and the key resource of this revolution, i.e. data. It provides a rationale that not only establishes relationships between technologies and data, but also highlights their roles as enablers or creators of value. Beyond showing the current focus of Industry 4.0 publications, this paper proposes a framework that is useful for tracking the evolution of the paradigm.
The rise of digital technology creates disruptive innovation, new opportunities in communication, it transforms industrial production and it changes the way we work, learn and live. That leads to the fourth Industrial Revolution, which changes the working world enormously. New skills, future-ready curricula, new learning and training concepts as well as the flexibility in education are necessary. To deliver that, it needs a fourth revolution in the educational system with a strong active presence and most important Artificial Intelligence (AI) driven features for better performance. Education 4.0 needs also a strong partnership between industry and academicals environment in the formation of human resources for Industry 4.0. We present in this paper our answers of education regarding the challenges of fourth revolution in Industry and the seven AI driven didactic methods identified for the University of Future.
DOI: 10.1109/SIITME.2017.8259942
Dyscalculia is a specific learning disability amongst learners in underachievement of learning Mathematics, which begins in childhood and is persistent through adulthood. The population of dyscalculia is estimated to range between 3% and 6% of the world population, including Malaysia. In this preliminary study, we highlight a data driven approach, through literature content analysis and interviews conducted upon teachers, to analyse the different terms used on dyscalculia, and the effectiveness of computer-based technologies or assistive learning technologies, which are developed and used for learners with learning problems in mathematics for the past two decades. Current studies show an increasing interest in adopting Augmented Reality (AR) technology in education, and in optimisming to create unique educational setting for special education learners, specifically Dyscalculia learners, to enable them to undergo experiential learning by experiencing learning through the real world, mixed with virtual objects without losing their sense of reality.
Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data—one that puts the relationship between teacher and student at the center. Our approach means that teachers’ pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation.
Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for datadriven development and optimization of educational technologies, focusing on intelligent tutoring systems. We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.
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