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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.

Digitalisation, big data and data analytics are hot topics for today's economic sectors. Given these trends, developing information technology capabilities of those who wish to join the public sector workforce has been emphasised in public administration teaching curricula. In this chapter, focusing on the public sector, we ask: What kind of data is published by governments and how can these data be used in teaching data analytics?

Against the backdrop of the growing importance of data analytics in public sector management curricula, this chapter uses the IMPACT cycle model to demonstrate how datasets from open government data portals can be used for teaching.

Several examples are shown to illustrate the different steps of the IMPACT cycle model. Concluding remarks include a reflection where potential caveats, dysfunctions and limits of data analytics are outlined.

The chapter explicitly focuses on ‘real world’ open government data published on open data portals. A holistic approach to data analytics ‘from start to finish’ is shown, including preparing datasets for the analysis and presenting results to line managers, whereas some of the previous work solely shows parts (e.g. testing the data).

In recent years professional accountancy bodies (e.g., CPA), accreditation institutions (e.g., AACSB) and employers have steadily raised, and continue to raise expectations regarding the need for accounting graduates to demonstrate skills in data analytics. One of the obstacles accounting instructors face in seeking to implement data analytics, however, is that they need access to ample teaching materials. Unfortunately, there are few such resources available for advanced programming languages such as R. While skills in commonly used applications such as Excel are no doubt needed, employers often take these for granted and incremental value is only added if graduates can demonstrate knowledge in using more advanced data analytics tools for decision-making such as coding in programming languages. This, together with the current dearth of resources available to accounting instructors to teach advanced programming languages is what drives motivation for this chapter. Specifically, we develop an intuitive, two-dimensional framework for incorporating R (a widely used open-source analytics tool with a powerful embedded programming language) into the accounting curriculum. Our model uses complexity as an integrating theme. We incorporate complexity into this framework at the dataset level (simple and complex datasets) and at the analytics task level (simple and complex tasks). We demonstrate two-dimensional framework by drawing on authentic simple and complex datasets as well as simple and complex tasks that could readily be incorporated into the accounting curriculum and ultimately add value to businesses. R script programming code are provided for all our illustrations.

In today’s marketplace, accountants must understand and master Big Data and data analytics, and many educators have devised approaches to help students acquire these critical skills. At our university, we have worked closely with our accounting advisory council to develop an adaptable classroom case where students not only gain a broad understanding of what data analytics means to the profession but also what specific tools are available to analyze an accounting-centered problem – cash collections. Using patterns and behaviors discovered in their data analyses, students develop collection procedures and controls for a case firm. Such a project begins to fulfill the profession’s initiative that accountants must exploit Big Data and data analytics for organizational growth and opportunity.

The accounting profession is beginning to demand data analytics skills from its professionals to handle the increasing amount of data available to address accounting questions. Indeed, the explosion of data availability and data are changing the accounting profession, providing accountants the opportunity to continue as key financial information providers to decision-makers. We conducted a survey of accounting department chairs to help understand if, when and how accounting programs would include data analytics in its curriculum. The authors find that 90.7% of accounting department chairs believe that data analytics belongs in the accounting curriculum, with 59.3% planning to introduce an accounting data analytics course in the next three to five years. Most (66.5%) prefer an accounting data analytics course as compared to the general business analytics course and more than half of respondents (56.2%) predict that their coverage of data analytics will be incorporated both throughout the regular accounting curriculum and in a standalone data analytics course. Combined with the requirement of 2018 Association to Advance Collegiate Schools of Business standards, the authors propose that data analytics should be incorporated both in the undergraduate level and graduate level, starting from basic analytics tools and ending with advanced emerging techniques.

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.

An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.

The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.

The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.

The paper tries to highlight the current trends and facets of data mining.

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 study unravels the intellectual structure of virtual reality (VR) in digital marketing (DM) research, identifies core research gaps and presents future research avenues. The study also conducts a performance analysis of publications in the field and identifies the most important stakeholders of the stated topic.

The Web of Science database was used to retrieve the publications that were relevant to the topic between 2012 and 2022. Biblioshiny, a shiny app for the Bibliometrix R package, is used to conduct a bibliometric analysis by decoding the results into several visual representations.

This report includes the most prolific contributors, keyword analysis results, productive nations, authors and connections, as well as the most often cited publications on VR in DM. In DM research, numerous perspectives on VR were looked at, explored and revealed.

The findings provide a new perspective and understanding of the issue for researchers in order to improve their research insights in the field. This study will also benefit marketing practitioners in ensuring the sustainability and innovativeness of technology used to run their DM campaigns.

This research provides the first bibliometric analysis of the citation works and productivity in the field of VR in DM using Biblioshiny, identifies core research gaps and provides future research agenda which can be useful to advance the research understanding in this context.

As faculty professional development increasingly occurs online and through social media, it becomes challenging to assess the quality of learning and effectiveness of programs and resources, yet it is important to evaluate such initiatives. The purpose of this paper is to explore how one faculty development center experimented with using analytics to answer questions about the use and effectiveness of its web and social media resources.

The case study was based on direct observation of the center’s practice and review of selected data generated by the analytic tools.

Unfortunately, while some analytics are available from a variety of sources, they are often distributed across tools and services. The center developed an analytics strategy to use data from Google Analytics and social media reporting tools to assess the use of online and social professional development resources. Initial results show that the center’s online and social professional development resources are widely used, both within and outside the university. However, more work is necessary to improve the strength and scope of the available analytics.

As a result of the analysis, the center has streamlined online resources, targeted social media use, and has begun developing methods to allow faculty to report online resource use as professional development for academic personnel purposes.

Many faculty development centers have not explored methods of evaluating online and social media resources. This paper outlines a strategic evaluation plan to measure the usage of online resources as well as engagement and interaction through social media.

Prior research (Harrast, Olsen, & Sun, 2023) analyzes the eight emerging topics to be included in future CPA exams and discusses their importance to career success and appropriate teaching locus in light of survey evidence. They find that the general topic of data analytics is the most important of the eight emerging topics. To further understand the topics most important to career success, this study analyzes subtopics underlying the eight emerging topics. The results show that advanced Excel analysis tools, data visualization, and data extraction, transformation, and loading (ETL) are the most important data analytics subskills for career success according to professionals and that these topics should be both introduced and emphasized in the accounting curriculum. The results provide useful information to educators to prioritize general emerging topics and specific subtopics in the accounting curriculum by taking into account the most pressing needs of the profession.