Big data academic and learning analytics: connecting the dots for academic excellence in higher education

Purpose Although big data analytics have great benefits for higher education institutions, due to lack of sufficient evidence on how big data analytics investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The current study proposes a big data academic and learning analytics enabled business value model to explain big data analytics potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the big data analytics current and potential benefits and business value creation path for big data academic and learning analytics success in higher education institutions. Findings The pressure of compliance with all legal & regulatory requirements and competition had pushed higher education institutions hard to adopt BDA tools. However, the study found out that application of risk & security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, our results provide new insights to higher education administrators on ways to create big data analytics capabilities for higher education institutions transformation and suggest an empirical foundation that can lead to more thorough analysis of big data analytics implementation. Originality/value A distinctive theoretical contribution of this study is its conceptualization of understanding business value from big data analytics in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of big data analytics and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking

Large, high-resolution displays carry the potential to enhance single display groupware collaborative sensemaking for intelligence analysis tasks by providing space for common ground to develop, but it is up to the visual analytics tools to utilize this space effectively. In an exploratory study, we compared two tools (Jigsaw and a document viewer), which were adapted to support multiple input devices, to observe how the large display space was used in establishing and maintaining common ground during an intelligence analysis scenario using 50 textual documents. We discuss the spatial strategies employed by the pairs of participants, which were largely dependent on tool type (data-centric or function-centric), as well as how different visual analytics tools used collaboratively on large, high-resolution displays impact common ground in both process and solution. Using these findings, we suggest design considerations to enable future co-located collaborative sensemaking tools to take advantage of the benefits of collaborating on large, high-resolution displays

A visual multivariate dynamic egocentric network exploration tool

Visualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this thesis, an exploratory visual analytics tool is proposed to display multivariate dynamic networks with spatial attributes. The proposed tool displays the distribution of multivariate temporal domain and network attributes in scattered views. Moreover, in order to expose the evolution of a single or a group of nodes in the dynamic network along the temporal axis, an egocentric approach is applied in which a node is represented with its neighborhood as an ego-network. This approach allows users to observe a node's surrounding environment along the temporal axis. On top of the traditional ego-network visualization methods, such as timelines, the proposed tool encodes ego-networks as feature vectors consisting of the domain and network attributes and projects them onto 2D views. As a result, distances between projected ego-networks represent the dissimilarity across temporal axis in a single view. The proposed tool is demonstrated with a real-world use case scenario on merchant networks obtained from a one-year long credit card transaction

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net

Understanding Cyberprivacy : Context, Concept, and Issues

Cyberprivacy has become one of the most worrisome issues in the age of digitalization, as data breaches have increased at an alarming rate, and the development of technology has changed privacy norms themselves. Thus, maintaining cyberprivacy is important for both academia and practitioners. However, the literature on cyberprivacy is fragmented, since the topic is multidisciplinary and often confused with cybersecurity and data privacy. In this study, we seek to understand cyberprivacy by conducting a comprehensive literature review and analyzing 79 selected articles on the topic between 2008 and 2021. Our analysis shows that there are eight contexts associated with cyberprivacy. We proposed concepts on cyberprivacy from different views and highlighted four issues related to cyberprivacy for future consideration. Taken together, the knowledge on cyberprivacy, its challenges and its practices does not seem to accumulate. Consequently, there is a need for more targeted research on the topic to cover different contexts.©2022 The Association for Information Systems (AIS), Friedrich-Alexander-Universität, International Conference on Wirtschaftsinformatik.fi=vertaisarvioitu|en=peerReviewed