Learning Any Time, Anywhere: Big Educational Data from Smart Devices

Along with the rapid development of technology, individuals today are required to align every aspect of their lives with the technological developments of the industrial revolution 4.0, consisting of artificial intelligence, the internet of things, and big data presented in society. Notably, it was related to education, including Islamic education, which frequently stereotyped about delays in responding to globalization's challenges. This preliminary study aims to encourage empirical research that is still lacking by exploring the role of big data in Islamic education and combining data from general education that has similar core. The study focused on using the scoping review method as a part of a literature review. As a result of this study, there are four impacted factors for strengthening the usage of big data: the performance and behavior factors of learning; the storage of education data; the update in the education system; and the use of big data in the education curriculum. Future studies should begin empirical research to elaborate more on these four impacted factors practically.

Big Data research has rapidly developed in the last 10 years, making it an interesting topic to investigate to understand the trends and developments of Big Data in Education. This study aims to analyze source documents and state contributions, and visualize research trends on the subject of Big Data in education during the last 10 years and identify potential research topics related to Big Data in Education in the future. This study uses a literature review and Meta-Analyses (PRISMA) method associated with bibliometric analysis using the Scopus and VOSViewer databases, through which 1,076 documents were obtained. The results of the bibliometric analysis show that Big Data research in education has increased significantly in the last 10 years. However, in the last year, it has decreased, this is the next challenge, and an opportunity for future research. The most common types of documents are conference papers, the source of most documents is conference proceedings, and the country that contributes the most is China. English is the most widely spoken language, and the authors with the highest contribution are Daniel, B. K. Further research related to Big Data can be used and implemented in the business world for Big Data analysis in education. Big Data can also be integrated with Science, technology, engineering, and mathematics (STEM) which can be a further research opportunity that can be applied to education because analyzing Big Data requires STEM learning skills. Thus, this research recommends finding updates in the study of Big Data in education by integrating STEM in education because analyzing Big Data again requires STEM learning expertise. This study has limitations in using only one database, namely Scopus, to obtain research data. Therefore, it is recommended that Big Data in Education research be conducted using other databases besides Scopus to obtain more extensive data.

Chapter 13 - Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors

Big Data refers to large and disparate volumes of data generated by people, applications and machines. It is gaining increasing attention from a variety of domains, including education. What are the challenges of engaging with Big Data research in education? This paper identifies a wide range of critical issues that researchers need to consider when working with Big Data in education. The issues identified include diversity in the conception and meaning of Big Data in education, ontological, epistemological disparity, technical challenges, ethics and privacy, digital divide and digital dividend, lack of expertise and academic development opportunities to prepare educational researchers to leverage opportunities afforded by Big Data. The goal of this paper is to raise awareness on these issues and initiate a dialogue. The paper was inspired partly by insights drawn from the literature but mostly informed by experience researching into Big Data in education.

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

In recent years, the Internet of Things (IoT) has constituted a driving force of modern technological advancement, and it has become increasingly common as its impacts are seen in a variety of application domains, including healthcare. IoT is characterized by the interconnectivity of smart sensors, objects, devices, data, and applications. With the unprecedented use of IoT in industrial, commercial and domestic, it becomes very imperative to harness the benefits and functionalities associated with the IoT technology in (re)assessing the provision and positioning of healthcare to ensure efficient and improved healthcare delivery. In this research, we are focusing on two important services in healthcare systems, which are cancer care services and business analytics/cloud services. These services incorporate the implementation of an IoT that provides solution and framework for analyzing health data gathered from IoT through various sensor networks and other smart devices in order to improve healthcare delivery and to help health care providers in their decision-making process for enhanced and efficient cancer treatment. In addition, we discuss the wireless sensor network (WSN), WSN routing and data transmission in the healthcare environment. Finally, some operational challenges and security issues with IoT-based healthcare system are discussed.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Due to technology advancements, the education sector has undergone major changes, particularly with the adoption of e-learning platforms and web services for student interactions. As a result, as many people log into these programmes, a large amount of data is being generated. Most academic institutions, however, have not yet completely tapped into this data, frequently merely handling the information connected to user queries while ignoring the crucial web behavioural inputs. This research attempts to provide a paradigm for using big data technologies to improve teaching and learning in educational institutions in order to address this issue.The document has three distinct goals. To begin with, list the advantages of big data technology in education, emphasising the potential gains for educational institutions. Second, to outline several big data applications in education and show how they might be applied to enhance educational procedures and results. The paper's final goal is to present specialised big data models that are designed for the educational system and provide a useful implementation framework.The research uses both survey and modelling methodologies to accomplish these goals. The survey's component uses review papers to describe the advantages and uses of big data in education while drawing conclusions from the body of existing research. The modelling approach, on the other hand, uses the Unified Modelling Language (UML) to offer a descriptive framework for comprehending the connections between little data and big data in the educational system. Big data covers the full learning process, including both students and teachers, while small data concentrates on conventional input about student performance alone.

In this paper, a systematic review of the latest research on the deployment of AI in educational Big data analytics is provided including its applications, challenges and future research possibilities. The databases IEEE Xplore, ScienceDirect, SpringerLink, Scopus, and Web of Science were systematically searched to find peer-reviewed journal articles written by Published by Elsevier B.V.8676,during 2020–2025. Fifty studies are included in the qualitative synthesis according to pre-designed inclusion/exclusion criteria. The results highlight four key themes for the application of AI: predictive analytics for predicting academic performance, personalised and adaptive learning systems, learning analytics for supporting teacher and institution decision making and institutional decision support using educational data. Challenges, including technical limitations, institutional readiness, and ethical considerations in privacy, bias, and transparency are also discussed.In addition, this analysis points to gaps in literature and paves ways for researchers to extend the current frontiers of research, including the design of interpretable AI models, multi-language tools, and privacy-preserving approaches. The findings offer important implications for researchers, educators, and policymakers striving to harness AI to design better and fairer educational systems.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

One of the most important issue that must be addressed in designing communication protocols for wireless sensor networks (WSN) is how to save sensor node energy while meeting the needs of applications. Recent researches have led to new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Internet of Things (IoT) is an innovative ICT paradigm where a number of intelligent devices connected to Internet are involved in sharing information and making collaborative decision. Integration of sensing and actuation systems, connected to the Internet, means integration of all forms of energy consuming devices such as power outlets, bulbs, air conditioner, etc. Sometimes the system can communicate with the utility supply company and this led to achieve a balance between power generation and energy usage or in general is likely to optimize energy consumption as a whole. In this paper some emerging trends and challenges are identified to enable energy-efficient communications in Internet of Things architectures and between smart devices. The way devices communicate is analyzed in order to reduce energy consumption and prolong system lifetime. Devices equipped with WiFi and RF interfaces are analyzed under different scenarios by setting different communication parameters, such as data size, in order to evaluate the best device configuration and the longest lifetime of devices.

Big data in education relate closely to a wealth of activities from the teachers, students and parents, as well as a substantial resources of knowledge that can be represented in hierarchical structures. The activities have characteristic of geolocation which can be projected onto a map, while the resources of knowledge can also be converted into the map. A map-based management and visual analysis method will largely benefit the users and the researchers from taking advantages of the big data in education. In this paper, we propose a novel map based method to manage and analyze the big data of mobile learning in education. With this method, the activities of users scattered among the space are reorganized on a geographic map with location changes in time series, and the resources are geo-tagged with the information from the developers or adopters, which are converted to a map style according to their hierarchical structures even when the users' information are unavailable. We first present the basic framework to organize the data by a map-based technology, and then a platform is proposed to perform the visual analysis. The method is adapt to the construction of massive online learning system and the mobile learning system of Central China Normal University to serve a national wide big data cloud learning program.

Big data, characterized by rich information, wide coverage, high value and universality, is applied in all fields including education. Educational researchers are focused on how to apply big data technology to the educational domain rationally. This paper examines the research status quo by reporting an analysis of research literature related to the application of big data in the fields of education, searched on the WOS (Web of Science). The analyses included the amount of literature, areas of distribution, journals in which the articles were published, research hotspots and research trends, using bibliometric analysis software CiteSpace and NoteExpress. The paper summarizes the research results, discusses the advantages and disadvantages of current research, and puts forward some suggestions that can be used as reference in later research of big data in education.

The availability of big data sets on the different learning modes has stimulated the applications of big data technology in education. This article is to introduce relevant application examples of education data mining to show how big data in education help to solve education issues. Firstly it lists the groups of target users and participants involved in education data mining, and the objectives to use data information of each group. It then goes on to illustrate the groups and their objectives further by given examples and finally conclude how big data help to solve education issues through reaching the objective of each group. The examples show that for students big data can generate personalized activities and offer academic advising, for teachers big data can reveal students' characteristics and predict future performance and offer assistance, for educational organizations big data can make scientific decisions.

Users face various privacy risks in smart homes, yet there are limited ways for them to learn about the details of such risks, such as the data practices of smart home devices and their data flow. In this paper, we present Privacy Plumber, a system that enables a user to inspect and explore the privacy "leaks" in their home using an augmented reality tool. Privacy Plumber allows the user to learn and understand the volume of data leaving the home and how that data may affect a user's privacy -- in the same physical context as the devices in question, because we visualize the privacy leaks with augmented reality. Privacy Plumber uses ARP spoofing to gather aggregate network traffic information and presents it through an overlay on top of the device in an smartphone app. The increased transparency aims to help the user make privacy decisions and mend potential privacy leaks, such as instruct Privacy Plumber on what devices to block, on what schedule (i.e., turn off Alexa when sleeping), etc. Our initial user study with six participants demonstrates participants' increased awareness of privacy leaks in smart devices, which further contributes to their privacy decisions (e.g., which devices to block).