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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.

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.

The explosive growth in the number of digital tools utilized in everyday learning activities generates data at an unprecedented scale, providing exciting challenges that cross scholarly communities. This paper aims to provide an overview of learning analytics (LA) with the aim of helping members of the information and learning sciences communities understand how educational Big Data is relevant to their research agendas and how they can contribute to this growing new field.

Highlighting shared values and issues illustrates why LA is the perfect meeting ground for information and the learning sciences, and suggests how by working together effective LA tools can be designed to innovate education.

Analytics-driven performance dashboards are offered as a specific example of one research area where information and learning scientists can make a significant contribution to LA research. Recent reviews of existing dashboard studies point to a dearth of evaluation with regard to either theory or outcomes. Here, the relevant expertise from researchers in both the learning sciences and information science is offered as an important opportunity to improve the design and evaluation of student-facing dashboards.

This paper outlines important ties between three scholarly communities to illustrate how their combined research expertise is crucial to advancing how we understand learning and for developing LA-based interventions that meet the values that we all share.

Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

The purpose of this paper is to present a systematic review of the mounting research work on learning analytics.

This study collects and summarizes information on the use of learning analytics. It identifies how learning analytics has been used in the higher education sector, and the expected benefits for higher education institutions. Empirical research and case studies on learning analytics were collected, and the details of the studies were categorized, including their objectives, approaches, and major outcomes.

The results show the benefits of learning analytics, which help institutions to utilize available data effectively in decision making. Learning analytics can facilitate evaluation of the effectiveness of pedagogies and instructional designs for improvement, and help to monitor closely students’ learning and persistence, predict students’ performance, detect undesirable learning behaviours and emotional states, and identify students at risk, for taking prompt follow-up action and providing proper assistance to students. It can also provide students with insightful data about their learning characteristics and patterns, which can make their learning experiences more personal and engaging, and promote their reflection and improvement.

Despite being increasingly adopted in higher education, the existing literature on learning analytics has focussed mainly on conventional face-to-face institutions, and has yet to adequately address the context of open and distance education. The findings of this study enable educational organizations and academics, especially those in open and distance institutions, to keep abreast of this emerging field and have a foundation for further exploration of this area.

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.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

In higher education, the authors serve multiple stakeholders with varying perspectives on the institution’s learning analytic system. The purpose of this paper is to highlight dual perspectives within the learning analytics (LA) system in one institution: that of an administrator responsible for college-wide improvement, and a faculty member responsible for programmatic improvement.

This manuscript provides a critical perspective with dual sets of experiences and viewpoints. This approach allows close examination of each perspective within the context of the existing literature, as the authors examine transitions in the use of LA.

These viewpoints offer insight into the interpretation of LA through the lens of various roles. In examining these viewpoints, the authors offer three actionable steps for other institutions who seek to implement.

These actionable steps offer a starting point for other institutions to engage in conversations related to the adoption of LA for continuous improvement across levels and roles. Relevant implications for various parties are discussed, with an emphasis on how administrators within the university system may support faculty to incorporate LA as part of their scholarly and teaching responsibilities.

Few studies have examined the perspectives of multiple stakeholders within an institution. Here the authors have presented these dual perspectives in order to further the dialogue between university administration and faculty as analytic systems become more widespread. Through this dialogue, the authors see increased opportunities for all stakeholders to better understand their role in LA.

The objective of this paper is to present a comprehensive review of the literature on learning analytics (LA) stakeholders' expectations to reveal the status of ongoing research in this area and to highlight gaps in research.

Conducting a literature review is a well-known method to establish knowledge and interpret the domain under examination (Snyder, 2019). In this paper, a descriptive approach was used to investigate the different research methods used to tackle LA stakeholders' expectations, which depends on summarising the studies' content and minimising the reviewer's interpretation.

Most of the studies have used interviews and focus groups without testing isolated variables. Moreover, it was concluded that LA awareness and perception are affected by a lack of resources, technical skills, trustworthiness, data availability, accessibility, organisational culture, integration and lack of technology. 10;This paper sheds light on research on LA stakeholders' expectations and highlights the need for investigating developing countries' context in order to test the perception and impact of LA. The study has focussed mainly on papers published in journals, which may have led to missing some previous research papers.

Literature review studies provide an overview of the existing LA literature from general focus to analytical methods. As it is a broad topic, this paper focuses on reviewing studies about stakeholders, which will give a fuller understanding of how their expectations are being discussed beyond the currently assumed benefits of LA.

The purpose of this paper is to describe examples of the application of learning analytics (LA), including the assessment of subject grades, identifying subjects that need revision, student satisfaction and cohort comparisons, to program curriculum review.

Examples of analyses that address specific questions that a curriculum review wishes to address are provided, together with examples of visualizations from the analyses to aid interpretation.

The results show that using LA as a part of curriculum review can provide insights not possible with the traditional curriculum review methods and can yield useful and actionable insights.

The work in this paper illustrates another important application for LA and demonstrates the value this approach has for informing curriculum enhancement at the program level.

The analyses described provide insights not possible with traditional curriculum review methods. However, the challenge remains to develop analytic tools that can assist teachers to conduct LA independently.

LA have been used to predict grades or identify at-risk students (Gaševic et al., 2016), but there is little research on its use for curriculum evaluation (Méndez et al., 2014). This paper addresses this gap and provides examples of its application in program curriculum review and the insights it can provide.