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Introduction: With the proliferation and amalgamation of technology and the emergence of artificial intelligence and the internet of things, society is now facing a rapid explosion in big data. However, this explosion needs to be handled with care. Ethically managing big data is of great importance. If left unmanageable, it can create a bubble of data waste and not help society achieve human well-being, sustainable economic growth, and development.

Purpose: This chapter aims to understand different perspectives of big data. One philosophy of big data is defined by its volume and versatility, with an annual increase of 40% per annum. The other view represents its capability in dealing with multiple global issues fuelling innovation. This chapter will also offer insight into various ways to deal with societal problems, provide solutions to achieve economic growth, and aid vulnerable sections via sustainable development goals (SDGs).

Methodology: This chapter attempts to lay out a review of literature related to big data. It examines the implication that the big data pool potentially influences ideas and policies to achieve SDGs. Also, different techniques associated with collecting big data and an assortment of significant data sources are analysed in the context of achieving sustainable economic development and growth.

Findings: This chapter presents a list of challenges linked with big data analytics in governance and achievement of SDG. Different ways to deal with the challenges in using big data will also be addressed.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

The continuous development of information technology leads to intelligent education research. In the context of internationalisation, the study aims to understand the relevant research status worldwide, research similarities and differences that need to be discovered, and research frontiers that need to be explored.

Web of Science (WoS) core collection was used as the data source, descriptive statistical analysis, geographic data visualisation and coupling analysis are used to reveal coupling relationships, present a cooperative situation and discover research frontiers.

Intelligent education research has been widely carried out in countries around the world, and there is extensive scientific research cooperation. According to coupling analysis results, the coupling strength of bibliographic between countries has been continuously improved, while the coupling strength of keywords has remained balanced, and there is standardisation and diversity of research.

The weakness of the research lies in the limitations of the data sources. Important research achievements on a certain topic in many non-English speaking countries are usually published in native journals. In the future research direction, more coupling analysis objects can be carried out, such as focussing on authors and institutions.

Through the coupling analysis of country bibliographic and keywords, it reveals the consistency and divergence of intelligent education research between different countries at different time spans.

Design and implement country bibliographic coupling (CBC) and country keyword coupling (CKC) strength indicators to calculate the strength of coupling between countries.

Despite several big data maturity models developed for businesses, assessment of big data maturity in the public sector is an under-explored yet important area. Accordingly, the purpose of this study is to identify the big data maturity models developed specifically for the public sector and evaluate two major big data maturity models in that respect: one at the state level and the other at the organizational level.

A literature search is conducted using Web of Science and Google Scholar to determine big data maturity models explicitly addressing big data adoption by governments, and then two major models are identified and compared: Klievink et al.’s Big Data maturity model and Kuraeva’s Big Data maturity model.

While Klievink et al.’s model is designed to evaluate Big Data maturity at the organizational level, Kuraeva’s model is appropriate for assessments at the state level. The first model sheds light on the micro-level factors considering the specific data collection routines and requirements of the public organizations, whereas the second one provides a general framework in terms of the conditions necessary for government’s big data maturity such as legislative framework and national policy dimensions (strategic plans and actions).

This study contributes to the literature by identifying and evaluating the models specifically designed to assess big data maturity in the public sector. Based on the review, it provides insights about the development of integrated models to evaluate big data maturity in the public sector.

In a socio-educational context impregnated by social networks, feminist organizations and individuals have turned to social media to spread their knowledge. This paper aims to approach how feminist tweets are produced to ignite meaningful informal learning (IL) processes.

This study uses an interdisciplinary mixed methodology. By using Twitter tracking tools, a database has been enabled to catalogue feminist hashtags into topics and categories for further analysis. These data have been contrasted with surveys to the managers of the most followed feminist accounts in Spain and Catalonia.

From an educational perspective, the analysed feminist hashtags have been organized in 13 different topic categories. The different propagation processes on Twitter – tweeting and retweeting – imply diverse learning processes. Moreover, tweets with complementary information such as images or links generate the most interaction, being the preferred format for IL.

Researching with Big Data in educational sciences is a field in development, and Twitter data collection tools are mostly addressed to marketing and economic sectors; thus, free tools with limited services were used, offering the analysis of a brief and concrete situation of a platform in constant change. Although this ephemeral data and its relevance does not prevail over time, it has an impact on citizens’ learning.

It is the first study in Spain that illustrates the informal education that feminism offers to the community, facing the complexity of measuring Twitter with an educational perspective through the use of marketing tools.

In the field of education in China, a large number of learning management systems have been deployed, in which vast amounts of data on learners and learning processes have been stored. How can one make use of these data? How can one transform the data into information and knowledge that inform decision-making in teaching and optimize learning? These questions have become a matter of concern for educators and learners. Learning analytics helps to unlock the value of the learning process data, so that the data can become an important basis for prudent decisions and process optimization. 'Learning analytics' was listed in the 2013 NMC Horizon Report as one of the emerging technologies that will have a great impact on learning, teaching and innovative research in higher education in two to three years. The report notes that learning analytics aims to decipher trends and patterns in the teaching and learning process from educational big data. In this paper, an online course on the Moodle platform is used for the research. The study examines reflection on online teaching and learning based on massive records of the learning process from the perspective of a tutor employing learning analytics. It is a brand new form of reflection on teaching and learning. The analysis of interactive course forums can help tutors to focus on key teaching and learning activities, and achieve more accurate analysis than with conventional face-to-face teaching activities. The research indicates that learning analytics is effective in supporting tutor reflection on interactive online teaching and learning.

Researchers now have more ways than ever before to capture information about groups of interest. In many areas, these are augmenting traditional survey approaches – in others, new methods are potential replacements. This paper aims to explore three key trends: use of nonprobability samples, mobile data collection and administrative and “big data.”

Insights and lessons learned about these emerging trends are drawn from recent published articles and relevant scientific conference papers.

Each new trend has its own timeline in terms of methodological maturity. While mobile technologies for data capture are being rapidly adopted, particularly the use of internet-based surveys conducted on mobile devices, nonprobability sampling methods remain rare in most government research. Resource and quality pressures combined with the intensive research focus on new sampling methods, are, however, making nonprobability sampling a more attractive option. Finally, exploration of “big data” is becoming more common, although there are still many challenges to overcome – methodological, quality and access – before such data are used routinely.

This paper provides a timely review of recent developments in the field of data collection strategies, drawing on numerous current studies and practical applications in the field.

The paper explores the emergence of the “big data regime” and the disruption that it is causing for the real estate industry. The paper defines big data and illustrates how an inductive, big data approach can help improve decision-making.

The paper demonstrates how big data can support inductive reasoning that can lead to enhanced real estate decisions. To help readers understand the dynamics and drivers of the big data regime shift, an extensive list of hyperlinks is included.

The paper concludes that it is possible to blend traditional and non-traditional data into a unified data environment to support enhanced decision-making. Through the application of design thinking, the paper illustrates how socially responsible development can be targeted to under-served urban areas and helps serve residents and the communities in which they live.

The paper demonstrates how big data can be harnessed to support decision-making using a hypothetical project. The paper does not present advanced analytics but focuses aggregating disparate longitudinal data that could support such analysis in future research.

The paper focuses on the US market, but the methodology can be extended to other markets where big data is increasingly available.

The paper illustrates how big data analytics can be used to help serve the needs of marginalized residents and tenants, as well as blighted areas.

This paper documents the big data movement and demonstrates how non-traditional data can support decision-making.

Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership.

To explore the role of AI in educational leadership, I synthesized the literature that intersects AI, decision-making, and educational leadership from multiple disciplines such as computer science, educational leadership, administrative science, judgment and decision-making and neuroscience. Grounded in the intellectual interrelationships between AI and educational leadership since the 1950s, this paper starts with conceptualizing decision-making, including both individual decision-making and organizational decision-making, as the foundation of educational leadership. Next, I elaborated on the symbiotic role of human-AI decision-making.

With its efficiency in collecting, processing, analyzing data and providing real-time or near real-time results, AI can bring in analytical efficiency to assist educational leaders in making data-driven, evidence-informed decisions. However, AI-assisted data-driven decision-making may run against value-based moral decision-making. Taken together, both leaders' individual decision-making and organizational decision-making are best handled by using a blend of data-driven, evidence-informed decision-making and value-based moral decision-making. AI can function as an extended brain in making data-driven, evidence-informed decisions. The shortcomings of AI-assisted data-driven decision-making can be overcome by human judgment guided by moral values.

The paper concludes with two recommendations for educational leadership practitioners' decision-making and future scholarly inquiry: keeping a watchful eye on biases and minding ethically-compromised decisions.

This paper brings together two fields of educational leadership and AI that have been growing up together since the 1950s and mostly growing apart till the late 2010s. To explore the role of AI in educational leadership, this paper starts with the foundation of leadership—decision-making, both leaders' individual decisions and collective organizational decisions. The paper then synthesizes the literature that intersects AI, decision-making and educational leadership from multiple disciplines to delineate the role of AI in educational leadership.

Student engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research.

The video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different modal data have different granularity, this study proposed the Fast–Slow Neural Network (FSNN) to detect engagement through both observable and internal information, with an asynchrony structure to preserve the sequence information of data with different granularity.

Experimental results show that the proposed algorithm can recognize engagement better than the traditional data fusion methods. The results are also analyzed to figure out the reasons for the better performance of the proposed FSNN.

This study combined multimodal data from observable and internal aspects to improve the accuracy of engagement detection in the classroom. The proposed FSNN used the asynchronous process to deal with the problem of remaining sequential information when facing multimodal data with different granularity.