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Article
Publication date: 30 October 2020

Hüsamettin Erdemci and Hasan Karal

Learning analytics enable learning to be reorganized through collecting, analyzing and reporting the stored data in online learning environment. One of the important…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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.

Details

The International Journal of Information and Learning Technology, vol. 38 no. 1
Type: Research Article
ISSN: 2056-4880

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Article
Publication date: 17 December 2018

Soraya Sedkaoui and Mounia Khelfaoui

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research…

Abstract

Purpose

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.

Design/methodology/approach

Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.

Findings

Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.

Research limitations/implications

This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.

Originality/value

Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

Details

Information Discovery and Delivery, vol. 47 no. 1
Type: Research Article
ISSN: 2398-6247

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Article
Publication date: 3 April 2007

William J. Hauser

The purpose of this paper is to discuss the current state of marketing analytics and how it should become a standard marketing research tool in the twenty‐first century.

Abstract

Purpose

The purpose of this paper is to discuss the current state of marketing analytics and how it should become a standard marketing research tool in the twenty‐first century.

Design/methodology/approach

The design of this paper is both a review of the field of marketing analytics and a discussion of how these factors must be enhanced and incorporated into twenty‐first century marketing research. As such this paper is offered as a viewpoint based on years of experience in the field and should serve as the basis for discussion and discourse by both academicians and practitioners.

Findings

In the realm of marketing, primary research has traditionally focused on quantitative or qualitative methodologies to provide customer insights. With advances in technology, especially data mining, marketing analytics has become an invaluable tool and should be viewed as an equal component of the marketing research toolkit. Analytics requires marketers to use data to understand customers at every touch point throughout their lifecycle with the business. To do this the analyst must mine, analyze, interpret, and present the information so that it is converted into actionable intelligence. In this process, the customer's information DNA is tracked, segmented, modeled and then acted upon. As these concepts and tools become standard operating procedures, academic marketing departments must internalize analytics into their overall curriculum in order to provide students with a compelling career advantage.

Originality/value

The value of this paper is that it presents marketers with a strong argument for the integration of marketing analytics into their practice of researching marketing issues and problems. Analytics completes the research triangle of qualitative, quantitative and data mined information gathering, analysis, and interpretation. It is hoped that this paper will generate additional discourse and research in this area and, especially, the adaptation of analytics as a standard research tool by marketers.

Details

Direct Marketing: An International Journal, vol. 1 no. 1
Type: Research Article
ISSN: 1750-5933

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Article
Publication date: 10 August 2018

Mohammad Kamel Daradkeh

Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to…

Abstract

Purpose

Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology.

Design/methodology/approach

This study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms.

Findings

Based on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations.

Research limitations/implications

This study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations.

Practical implications

This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals.

Originality/value

This study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.

Details

Information Technology & People, vol. 32 no. 3
Type: Research Article
ISSN: 0959-3845

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Article
Publication date: 9 May 2018

Soraya Sedkaoui

The rise of big data and analytics companies has significantly changed the business playground. Big data and the use of data analytics are being adopted more frequently…

Abstract

Purpose

The rise of big data and analytics companies has significantly changed the business playground. Big data and the use of data analytics are being adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in the dynamic processes. Working with big data and applying a series of data analysis techniques require strong multidisciplinary skills and knowledge of statistics, econometrics, computer science, data mining, law and business ethics, etc. Higher education institutions (HEIs) are concerned by this phenomenon which is also changing learning needs and require a reorientation toward the development of novel approaches and advancements in their programs. The purpose of this paper is to introduce and define big data analytics as having an immense potential for generating value for businesses. In this context, one prominent strategy is to integrate big data analytics in educational programs to enrich student’ understanding of the role of big data, especially those who want to explore their entrepreneurial ways and improve their effectiveness. So, the main purpose of this article consists, on the one hand, in why HEIs must carefully think about how to provide powerful learning tools and open a new entrepreneurship area in this field, and, why, on the other hand, future entrepreneurs (students) have to use data analytics and how they can integrate, operationally, analytics methods to extract value and enhance their professional capabilities.

Design/methodology/approach

The author has established an expert viewpoint to discuss the notion of data analytics, identify new ways and re-think what really is new, for both entrepreneurs and HEIs, in the area of big data. This study provides insights into how students can improve their skills and develop new business models through the use of IT tools and by providing the ability to analyze data. This can be possible by bringing the tool of analytics into the higher educational learning system. New analytics methods have to help find new ways to process data and make more intelligent decisions. A brief overview of data analytics and its roles in the context of entrepreneurship and the rise of data entrepreneur is then presented. The paper also outlines the role of educational programs in helping address big data challenges and transform possibilities into opportunities. The key factors of implementing an efficient big data analytics in learning programs, to better orientate and guide students’ project idea, are also explored. The paper concludes with suggestions for further research and limitations of the study.

Findings

The findings in this paper suggest that analytics can be of crucial importance for student entrepreneurial practice if correctly aligned with their business processes and learning needs and can also lead to significant improvement in their performance and quality of the decisions they make. The added value of big data is the ability to identify useful data and turn it into usable information by identifying patterns and exploiting new algorithms, tools and new project solutions. So, the move toward the introduction of big data and analytics tools in higher education addresses how this new opportunity can be operationalized.

Research limitations/implications

There are some limitations to this research paper. The research findings have significant implications for HEIs in the field of analytics (mathematics and computer science), and thus, it is not generalizable with any further context. Also, the viewpoint is centered on the data analytics process as a value generator for entrepreneurial opportunities.

Originality/value

This research can be considered as a contribution with literature about educational quality, entrepreneurship and big data analytics. This study describes that new analytics thinking and computational skills are needed for the newer generation of entrepreneurs to handle the challenges of big data. But, preparing them to capture, analyze, store and manage the large amounts of data available today – so they can see value in data – is not just about implementing and using new technologies. This is also, about, a dynamic, operational and modern educational learning process from which a student can extract the maximum benefit. In another words: How to make new opportunities from these data? Which data to select for the analysis? and How to efficiently apply analytical techniques to generate value?

Details

International Journal of Innovation Science, vol. 10 no. 2
Type: Research Article
ISSN: 1757-2223

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Article
Publication date: 5 June 2017

Kevin Daniel André Carillo

The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a…

Abstract

Purpose

The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era.

Design/methodology/approach

The approach involves analysis and review of the literature.

Findings

The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education.

Research limitations/implications

For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry.

Practical implications

The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world.

Originality/value

By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.

Details

Business Process Management Journal, vol. 23 no. 3
Type: Research Article
ISSN: 1463-7154

Keywords

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Article
Publication date: 4 September 2020

Jing Lu, Lisa Cairns and Lucy Smith

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight…

Abstract

Purpose

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Design/methodology/approach

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Findings

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

Research limitations/implications

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Originality/value

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

Details

Journal of Modelling in Management, vol. 16 no. 2
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 11 September 2017

Maurice McNaughton, Lila Rao and Gunjan Mansingh

The purpose of this paper is to describe an agile approach to academic analytics that is currently being applied on one of the campuses of a leading higher educational…

Abstract

Purpose

The purpose of this paper is to describe an agile approach to academic analytics that is currently being applied on one of the campuses of a leading higher educational institution in the Caribbean. This agile approach enables the rapid development of a strategic analytics roadmap and proof-of-concept analytics applications for the institution.

Design/methodology/approach

The approach was developed using Design Science which involves the development and rigorous evaluation of an artifact. The agile approach is the artifact and the design evaluation was done using the observational method of primary cases studies where the artifact is studied in depth in a business environment, in this case this was a leading higher educational institution in the Caribbean.

Findings

The final output, the roadmap, highlights the importance of a balanced portfolio of analytics initiatives, relevant and tailored to the institution’s specific context that includes technology and applications projects, as well as capacity building, organizational structures and policy initiatives.

Research limitations/implications

The approach that was used and the specific techniques proposed can be extended by other researchers and in so doing will increase the body of research as it relates to agile analytics.

Practical implications

The approach will be beneficial to educational institutions that are considering how best to harness the strategic value of its data. The analytics roadmap will allow the institution to be clear about the path they should take to maximize their investments in analytics initiatives.

Originality/value

A number of existing well-accepted research techniques have been synthesized in the development and application of this agile approach. The approach and final roadmap consider the institution’s readiness for and understanding of what is involved in analytics before investing significant resources in its adoption.

Details

Journal of Enterprise Information Management, vol. 30 no. 5
Type: Research Article
ISSN: 1741-0398

Keywords

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Article
Publication date: 14 July 2020

Salvatore V. Falletta and Wendy L. Combs

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for…

Abstract

Purpose

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.

Design/methodology/approach

Conceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.

Findings

HR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.

Practical implications

The HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.

Originality/value

This paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.

Details

Journal of Work-Applied Management, vol. 13 no. 1
Type: Research Article
ISSN: 2205-2062

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Article
Publication date: 21 September 2015

Anthony Marshall, Stefan Mueck and Rebecca Shockley

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for…

Abstract

Purpose

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.

Design/methodology/approach

Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.

Findings

Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.

Research limitations/implications

Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.

Practical implications

Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.

Originality/value

The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.

Details

Strategy & Leadership, vol. 43 no. 5
Type: Research Article
ISSN: 1087-8572

Keywords

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