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A literature review of 28 data literacy, education articles from 2010 to 2018 was conducted to gain a better understanding of the current state of data literacy research.

A systematic literature review of ERIC, Education Source, and JSTOR was conducted. Articles were included in this literature review if they focused on “data literacy” for K-12 teachers or leaders.

Results demonstrated that the concept of data literacy has become more concrete, but there is still disagreement about the parameters of the construct. While data literacy was shown to be gaining in importance, training from schools of education were focused heavily on assessment literacy. Four recommendations are made as follows: (1) create skill-focused educator prep programs, (2) encourage opportunities for collaboration, (3) model data use from both quantitative and qualitative sources and (4) investigate the role of technology and big data on data literacy.

The scope of this literature review was very narrow and, as such, does not fully encapsulate data-driven decision-making in K-12 education overall.

Data literacy is important for both teachers and leaders, as educational environments strive to better understand individual learners and improve learning outcomes. This literature review looks to pull together the current status of data literacy research with hopes of inspiring more targeted research that influences training practices for both teachers and leaders.

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.

This paper aims to examine the influence of big data (BD) on human resource management (HRM). It defines how these data can be a useful tool in the decision-making process of companies’ human resources to obtain a sustainable competitive advantage.

This paper emphasizes the need to develop a holistic approach to emphasize these relations. Starting from these observations, the document proposes empirical research employing Eurostat data to test the benefits of BD in HRM decisions that optimize the relationship between training, productivity, and well-being.

The findings estimate HRM decisions and their impact in a broader macroeconomic and microeconomic perspective.

BD research is emerging as a crucial discipline in human resources. To overcome this problem, the paper develops an analysis of the literature on cleaner production and sustainability context; it creates a conceptual framework to clarify whether the existing studies consider the growing intensity of BD on human resources.

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.

The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.

The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.

The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc.

The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation.

Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences.

Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

This study aims to investigate the preferences of 96 educational researchers on the use of digital technologies in scientific research.

The study was designed as a quantitative-dominant sequential explanatory mixed-method research.

Despite the spreading use of advanced technologies of big data and data mining, the most preferred digital technologies were found to be data analysis programs, databases and questionnaires. The primary reasons of using digital technology in scientific research were to collect data easily and quickly, to reduce research costs and to reach a higher number of participants.

The use of digital technologies in scientific research is considered a revolutionary action, which creates innovative opportunities. Through digitalized life, probably for the first time in history, the educational researchers have analytical information, which we can benefit from more than the individual's own statements in research involving human factor. However, there are a few studies that investigated the preferences of educational researchers who use digital technologies in their scientific research.

This study aims to investigate the futuristic visions of PhD students at Distance Education department of Anadolu University on the use of learning analytics (LA) and mobile technologies together.

This qualitative research study, designed in the single cross-section model, aimed to reveal futuristic visions of PhD students on the use of LA in mobile learning. In this respect, SCAMPER method, which is also known as a focused brainstorming technique, was used to collect data.

The findings of the study revealed that the use of LA in mobile can solve everyday problems ranging from health to education, enable personalized learning for each learner, offer a new type of evaluation and assessment and allow continuous feedback and feedforwards; yet this situation can also arise some ethical concerns since the big data collected can threaten the learners by interfering with their privacy, reaching their subconscious and manipulating them as well as the whole society by wars, mind games, political games, dictation and loss of humanity.

The research is limited with the views of six participants. Also, the sample of the study is homogeneous in terms of their backgrounds – their age range, their departments as PhD students and their fields of expertise.

The positive perceptions of PhD students provide a ground for the active use of LA in mobile. Further, big data collected through LA can help educators and system makers to identify patterns which will enable tailored education for all. Also, use of LA in mobile learning may stimulate the development of a new education system including a new type of evaluation and assessment and continuous feedback and feedforwards.

The widespread use of mobile technologies opens new possibilities for LA in the future. The originality of this research comes from its focus on this critical point.

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.

The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.

As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.

The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.