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![Companies in all sectors have at least 100 terabytes of stored data in the United States; many have more than 1 petabyte [14]](profile/Athanasios-Drigas/publication/274890131/figure/fig5/AS:391804554891268@1470424893281/Companies-in-all-sectors-have-at-least-100-terabytes-of-stored-data-in-the-United-States.png)
Companies in all sectors have at least 100 terabytes of stored data in the United States; many have more than 1 petabyte [14]
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![Table 1: Benefits of Big Data and Open Data in Education [10]](profile/Athanasios-Drigas/publication/274890131/figure/tbl1/AS:391804554891270@1470424893383/Benefits-of-Big-Data-and-Open-Data-in-Education-10_Q320.jpg)
![Fig. 1 How open data relates to other types of data [10]](profile/Athanasios-Drigas/publication/274890131/figure/fig1/AS:391804550696970@1470424892777/How-open-data-relates-to-other-types-of-data-10_Q320.jpg)
![Fig. 3 Deep analytical talent: Where are they now? [16]](profile/Athanasios-Drigas/publication/274890131/figure/fig3/AS:391804554891264@1470424893070/Deep-analytical-talent-Where-are-they-now-16_Q320.jpg)
![Fig. 4 Use of Data Analytics with Big Data in Industry [17] Furthermore...](profile/Athanasios-Drigas/publication/274890131/figure/fig4/AS:391804554891267@1470424893126/Use-of-Data-Analytics-with-Big-Data-in-Industry-17-Furthermore-as-we-can-mention-from_Q320.jpg)
This paper is a study on the use of Big Data in Education. Analyzed how the Big Data and Open Data technology can actually involve to education. Furthermore how big mounts of unused data can benefit and improve education. Providing some new tools and methods bypassing the traditional difficulties and open a new way of education.
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On October 17, 2017, the ODI sponsored a day-long Standard Reference Data Workshop. More than 120 NIST staff members and associates from four NIST laboratories and three offices registered for this workshop. The workshop format consisted of presentations, panel sessions, and breakout sessions. Topics included the current state of the SRD Program, t...
Citations
... The advent of big data offers unprecedented opportunities to harness extensive datasets for nuanced understanding and tailored educational strategies [1]. Studies increasingly highlight the role of learner data in crafting dynamic learning environments that respond in real-time to the needs of the individual [2]. Yet, a significant gap remains in comprehensive frameworks that integrate these vast data reservoirs effectively into language education practices. ...
This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model's adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences.
... Policymakers are being forced by this disruption in the delivery of education to figure out how to increase commitment at scale while ensuring full e-learning arrangements and managing the digital environment. In numerous nations, the employment of big data in schools and universities is normal (Olanrewaju et al., 2016;West, 2012;Drigas and Leliopoulos, 2014;Sedkaoui and Khelfaoui, 2019;Elia et al., 2019;Maldonado-Mahauad et al., 2018;Cantabella et al., 2019). ...
... Big data opens up opportunities for new learning experiences for students, enabling them to share information with educational institutions and expand their knowledge and skills. Moreover, educational institutes and universities can utilise big data to assist and prepare their future students (Drigas & Leliopoulos, 2014). ...
Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of student comments is being undertaken. The first and simplest use of big data analytics is for the identification of high frequency keyword groups, which, without big data analytics, would be extremely time consuming. However, the lack of context surrounding keyword groups severely limited the ability to draw meaningful conclusions and highlighted the need for human intervention in the analysis process. Future work includes sentiment analysis. This initial work is an impetus for further exploration of big data analytics methods in qualitative contexts, especially in dynamic contexts where rapid data analysis can form a basis for timely interventions.
... In this way, big data in the field of education can be used to support the educational system. Its techniques provide reviews of existing academic performance (Drigas & Leliopoulos, 2014). This is because educational institutions, as indicated by Raopn & Baglodi (2018), did not use this new generation of data and did not apply their techniques to classify and extract large educational data sets to the fullest extent. ...
... This framework is studied, understood and used as a model for current analytics across different stages and levels in the supply chain and proper utilization of the computational framework will help to optimize results obtained from the analytics process [22]. A computational framework is required to ensure that data processing, despite how endless and relentless it has become, is context-dependent [23]. This statement means that the data is required to be processed according to the needs and requirements of the data users and not abstractly or generally, but according to the information required to make decisions or recommendations based on the evidence available which can be gotten through the information garnered concerning the textiles. ...
The ever-growing degradation of the environment has led to the building and implementation of various strategies to achieve sustainable development. The textile industry is not left out in this struggle to reach sustainability through sustainable supply chain management. This paper proposes a sustainable closed-loop supply chain for the textile industry, integrating some industry 4.0 Technologies to facilitate data-driven decision-making within the supply chain and ensure sustainability. The study reveals that block-chain application in the closed-loop supply chain will support tracking and enable supply chain traceability and provide secure means of recording information about the actors and their actions in the supply chain. The paper further recommends the use of Big Data technologies throughout the life cycle stages for data retrieval, processing, storage, mining and application in the textile industry. Also, Industry 4.0 technologies will facilitate sustainable supply chain in the textile industry as it helps in data collection, analysis and information transmission.
... The concept of big data is underpinned by the massive increase in the volume, structure, and speed with which data is generated (Daniel, 2017). Educators can analyze and improve the traditional educational system through the usage of big data (Drigas and Leliopoulos, 2014). The key accomplishment of learning analytics in recent years may be identified as the growth of digital learning, which has improved the quality and accessibility of educational data (Sghir et al., 2023). ...
The contemporary era’s extensive use of data, particularly in education, has provided new insights and benefits. This data is called ‘education big data’, and the process of learning through such data is called ‘learning analytics’. Education in big data and learning analytics are two important processes that produce impactful results and understanding. it is crucial to take advantage of these processes to enhance the current education system. We conduct a bibliometric analysis based on the PRISMA statement template. The publications used for the analysis are based on the years 2012–2021. We examine and analyze a total of 250 publications, mainly sourced from the Scopus database, for insights regarding education big data and learning analytics. All of the publications also undergo filtration according to specific inclusion and exclusion criteria. Based on the bibliometric analysis conducted, we discover the distribution of education big data and learning analytics publications across the years
2012–2021, the most relevant journals and authors, the most significant countries, the primary research keywords, and the most important subject area involved. This study presents the trends and recommendations in education big data and learning analytics. We also offer suggestions for improvement and highlight the potential for enhancement of the education system through the full utilization of education big data and learning analytics.
... The precision education approach aims to personalize the learning experience by taking into account the unique characteristics and needs of each student, including their learning environments and strategies (Hart, 2016;Luan et al., 2020), to improve the diagnosis, predication, and treatment of learning outcomes (Lu et al., 2018). Previous research in the field of education has encountered difficulties in providing real-time, actionable feedback based on the learning data collected from and about students, as well as other stakeholders such as teachers and administrators (Drigas & Leliopoulos, 2014;Madhavan & Richey, 2016). Precision education techniques have the potential to alleviate this issue. ...
In early 2020, the COVID-19 pandemic prompted higher education institutions worldwide to transition on-site courses to emergency remote teaching environments (ERTEs). In this context, it was unclear what factors were associated with students’ learning outcomes and how students could be grouped based on their responses to these factors. Through exploratory factor analysis and structural equation modeling analysis on 9418 higher education students in 41 countries attending courses in ERTEs during COVID-19, we identified six environmental or individual latent factors that had significant associations with learning outcomes: perceived teacher support (TS), satisfaction with administration support (AS), satisfaction with synchronous course organization (SCO), satisfaction with asynchronous course organization (ACO), computer skills (CS), and worry about life. Among these factors, SCO had the most significant direct association, followed by CS, ACO, AS, and TS. The associations of these factors with learning outcomes were partially mediated by students’ positive and negative academic emotions. Using latent profile analysis, we further identified eight profiles among students, representing combinations of the six environmental and individual factors, and found that different profiles were related to varying learning outcomes and student characteristics. These findings highlight important factors associated with higher education students’ learning outcomes in ERTEs during crisis situations and provide implications for individualized interventions and support strategies to enhance learning outcomes.
... The precision education approach aims to personalize the learning experience by taking into account the unique characteristics and needs of each student, including their learning environments and strategies (Hart, 2016;Luan et al., 2020), to improve the diagnosis, predication, and treatment of learning outcomes (Lu et al., 2018). Previous research in the field of education has encountered difficulties in providing real-time, actionable feedback based on the learning data collected from and about students, as well as other stakeholders such as teachers and administrators (Drigas & Leliopoulos, 2014;Madhavan & Richey, 2016). Precision education techniques have the potential to alleviate this issue. ...
In this study, we aimed to explore the factors associated with higher education students’ learning outcomes in emergency remote teaching environments (ERTEs) during the COVID-19 pandemic at both the population and individual levels, given the limited understanding in previous research. 9418 students from 41 countries were selected for analysis from a survey-based dataset that was collected with the aim of understanding the self-perceived impacts of the first-wave COVID-19 pandemic on higher education students. We conducted structural equation modeling to explore associated factors and latent profile analysis to identify student profiles based on these factors. Utilizing the identified profiles, we developed a random forest-based classifier to identify the membership of students’ profiles. The results showed that six environmental and individual factors—partially mediated by academic emotions—were significantly associated with learning outcomes. The positively associated factors, ranked by path coefficient, were satisfaction with synchronous course organization (SCO), computer skills (CS), satisfaction with asynchronous course organization (ACO), satisfaction with administration support (AS), and perceived teacher support (TS). The negatively associated factor was worry about life. Based on these factors, eight profiles were identified with varying learning outcomes and student characteristics. The classifier achieved a testing accuracy of 0.904. By integrating variable-centered and person-centered approaches, this study bridges the gap in understandings of general patterns and individual differences regarding key factors associated with higher education students’ learning outcomes. The findings have implications for designing individualized interventions and support strategies to enhance student learning outcomes and mitigate educational disparities in ERTEs during crisis situations.
... This new educational service is a total change that has never happened before, virtual learning, digital learning resources, online administration. Employing technological intelligence for data analysis and using large data for analysis (Drigas & Leliopoulos, 2014). These activities before digital transformation were done manually and it takes a long time to get effective work results. ...
Purpose: The perpuse of this research is to know and describe in depth, detail, and is oriented towards developing a theory based on findings about the digital transformation process in schools. Theoretical framework: The impact of changing lifestyles in the era of digital technology is a more systematic, effective, and efficient performance. Madrasah principals play a major role in digital transformation in Madrasa Method/design/approach: The method in this study is to use a qualitative approach with one subject from the principal of the Madrasah Ibtidaiyah Muslimat Nahdhatul Ulama, Sidoarjo. Data collection techniques in this study include interviews, participant observation, and documentation. Results and conclusion: The results of the study show that the digital skills of madrasa principals have an impact on the digital transformation process in madrasas. Madrasah digital transformation appears explicitly in the achievement indicators of the vision and the formation of a digital team under the coordination of the Sarpras team. it is also a guideline for carrying out digital transformation with various changes, including; creating madrasa settings, effective and efficient digital technology management, inspiring work climate, organizational culture, digital madrasa environment. Implications of the research: This research contributes to the application of digital transformation in schools so that technology management is more effective. Originality/value: The results obtained in this study are innovative and relevant for school principals, in the context of managing digital transformation in schools.
... Finally, we have to underline the role of digital technologies in the education domain that are very productive and successful, facilitate and improve assessment, intervention, and educational procedures via Mobiles [60][61][62][63][64][65][66][67][68], various ICTs applications [56], , AI and STEM [94][95][96][97][98][99][100][101][102][103], and games [104][105][106][107]. Additionally, the combination of ICTs with theories and models of metacognition, mindfulness, meditation, and emotional intelligence cultivation [25], [26], [27], [28], [31], [108][109][110][111][112][113][114][115][116][117][118][119][120] accelerates and improves more over educational practices and results. ...
Technology is developing at a rapid pace, affecting the socioeconomic situation of the planet through innovation and the evolution of applications for easier and faster access to goods and services. The development of new technologies has also affected education. In this paper, the school of the future is presented with regard to emerging or exponential disruptive technologies and the impact of emotional intelligence on those involved in education. The politics of globalization, global perspectives, perceptions, and contemporary social values lead to the education of individuals with accessibility for everyone, from everyone , and from everywhere and at any time in an inclusive world. The school is changing. Research showed that the school of the future reshapes its learning environment to meet the increasing demands of the 21st century which are positively correlated with dynamic, flexible, interactive, creative, and self-directed learning technologies.
