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This paper aims to present a review of case studies on the use of learning analytics in Science, Technology, Engineering, (Arts), and Mathematics (or STE[A]M) education. It covers the features and trends of learning analytics practices as revealed in case studies.

A total of 34 case studies published from 2013 to 2018 reporting relevant learning analytics practices were collected from Scopus and Google Scholar for analysis. The features and trends of practices were identified through a comparison of the first (2013–2015) and the second period (2016–2018).

The results showed an increasing adoption of learning analytics in STE(A)M education, particularly in the USA and European countries and at the tertiary level. More specific types of data have been collected for the learning analytics practices, and the data related to students’ learning processes has also been more frequently used. The types of STE(A)M learning practices have become more diversified, with technology enhancement features increasingly introduced. The outcomes of the case studies reflect the overall benefits of learning analytics and address the specific needs of STE(A)M education. There have also been fewer types of limitations encountered in the learning analytics practices over the years, with unknown correlation among variables, small sample size and limited data being the major types.

This study reveals the implementation of learning analytics in relation to the contexts and needs of STE(A)M education. The findings also suggest future work for examining the adoption of learning analytics to cope with the development of STE(A)M and, in particular, how the successful experience of learning analytics in other disciplines could be transferred to STE(A)M.

The purpose of this paper is to examine the role of data analytics in addressing contemporary challenges and opportunities facing institutions of higher education, with a special emphasis on enrollment management for the example of a college of business administration in a Hispanic-serving four-year institution of higher education.

After reviewing previous research on this subject, the authors propose adding a new stratum to higher education analytics, enrollment management analytics, which uniquely identifies the actions associated with attracting and recruiting students, accounts for institutional differences and leads to students’ successful matriculation at institutions.

The authors demonstrate on the example of a professional college in a four-year institution of higher education how the utilization of modern analytical tools and techniques develop actionable decisions in the students’ recruitment process, which leads to their matriculation.

A definition and a new stratum in higher education analytics are proposed for enrollment management analytics.

The potential capabilities and benefits that learning analytics can provide are not fully utilized. A current stream of research suggests that learning analytics has more to offer for continuous improvement of higher education institutions. This study aims to explore the opportunities that data analytics stand to offer higher education and the challenges that plays down its role, adoption and usage in different areas of higher education institutions.

This study adopts a systematic literature review approach in answering the research questions. The critical role of learning analytics and the exploratory nature of research questions justify the use of systematic literature review. The current study used systematic research process adapted and presented by Hallinger (2013) to be used in social sciences in general and in educational leadership and management in particular. A standard process of finding relevant articles and examining reference lists is followed using articles from higher education which is the research context.

An examination of the literature showed that the majority of studies within the sample of articles are empirical representing 53 per cent, 32 per cent are conceptual, while only 15 per cent of the articles are a systematic literature review. Results also show that 58 per cent of the articles are teaching and learning related, 34 per cent are management related, while only 8 per cent are research related. Several challenges and opportunities of learning analytics in the three areas highlighted are presented and discussed.

The benefits and challenges of learning analytics are numerous and scattered in the literature. In this study, a typology related to different educational domains is developed to shed light on the benefits and challenges of learning analytics within particular higher education areas that are relevant to specific stakeholders. Benefits and challenges of learning analytics are classified into being management related, teaching and learning related and research related.

This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning analytics focuses on the practice of using data about students to inform interventions aimed at improving outcomes (e.g., retention, graduation, and learning outcomes), and it is a rapidly growing area of educational practice within higher education institutions (HEIs). This growth is spurring a number of commercial developments, with many companies offering “analytics solutions” to universities across the world. We review the origins of learning analytics and identify drives for its growth. We then discuss some possible implications for this growth, which focus on the ethics of data collection, use and sharing.

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics?

A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education.

The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching.

The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

In spite of the popularity of learning analytics (LA) in higher education institutions (HEIs), the success rate and value gained through LA projects is still little and unclear. The existing research on LA focusses more on tactical capabilities rather than its effect on organizational value. The key questions are what are the expected benefits for the institution? And how the investment in LA can bring tangible value? In this research, the authors proposed a value realization framework from LA extending the existing framework of information technology value.

The study includes a detailed literature review focusing on the importance, existing frameworks and LA adoption challenges. Based on the identified research gap, a new framework is designed. The framework depicts the several constructs and their relationships focusing on strategic value realization. Furthermore, this study includes three case studies to validate the framework.

The framework suggests that leveraging LA for strategic value demands adequate investment not only in data infrastructure and analytics but also in staff skill training and development and strategic planning. Universities are required to measure the strategic role of LA and spend wisely in quality data, analytical tools, skilled staff who are aware of the latest technologies and data-driven opportunities for continuous improvement in learning.

The framework permits education leaders to design better strategies for attaining excellence in learning and teaching, and furnish learners with new data to settle on the most ideal decisions about learning. The authors believe that the appropriation of this framework and consistent efficient interest in learning analytics by the higher education area will prompt better results for learners, colleges and more extensive society. The research also proposes two approaches and eleven research agendas for future research based on the framework. The first is based on the constructs and their relationships in LA value creation, whereas the later one focusing on identifying problems associate with it.

Learning analytics enable learning to be reorganized through collecting, analyzing and reporting the stored data in online learning environment. One of the important agents of education process is the instructors. How the use of learning analytics within education process is evaluated by the instructors is important. The purpose of this study is to determine the experiences of instructors in relation to the use of learning analytics.

In this study, data were collected from instructors through interviews to determine the reflections of learning analytics on the education process. While qualitative study method was adopted, phenomenological design was used.

As a result of analysis of findings, it was concluded that the use of learning analytics in the education process was beneficial. It was established that learning analytics were helpful in the self-assessment of instructors' performances, making early intervention to risky students and creating a lesson plan.

This study was carried out in a foreign language course and with five academicians during one semester.

This study aims to reveal the experiences of the instructors on the use of learning analytics and present scientific findings on a subject on which a limited number of studies have been conducted. With the start of learning analytics' use in the educational process, some concerns have been raised. This study tries to respond to the various concerns of instructors who intend to use learning analytics in the process.

The use of learning analytics is gradually increasing. In the studies conducted, it is seen that the studies have focused on the effect of learning analytics on the learning outputs of students. It is important to determine how instructors, who are the other important elements of the process, make use of learning analytics and how their experiences regarding the use of learning analytics are. The focal point of this study is to reveal the impact of learning analytics on the education process from the perspective of instructors.

This paper aims to review and identify the major patterns and trends in learning analytics practices in higher education institutions. The review covers the characteristics of the institutions, as well as the characteristics and outcomes of the learning analytics practices.

This research collected literature published in 2011-2016 which reported learning analytics practices from Scopus and Google Scholar, covering a total of 47 institutions, and categorised the information about the relevant institutions and practices.

The results show that most of the institutions were public ones in the USA and the UK of various sizes and offering different levels of study. The learning analytics practices were mainly institution-wide, apart from a small number focusing on selected courses. The purposes of the practices were mainly to enhance the effectiveness of learning support and administration, followed by facilitating students’ learning progress. The most common types of data collected for the practices were students’ academic behaviours and their background information. Positive outcomes were reported for a majority of the practices, and the most frequent ones being an increase in cost-effectiveness and understanding of students’ learning behaviours. Other outcomes included the improvement of student retention, timely feedback and intervention, support for informed decision-making and the provision of personalised assistance to students.

The results provide an overview of the use of learning analytics in the higher education sector. They also reveal the trends in learning analytics practices, as well as future research directions.