Data Analytics in Higher Education: Key Concerns and Open Questions

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

Multimedia big data computing for in-depth event analysis

While the most part of ”big data” systems target text-based analytics, multimedia data, which makes up about 2/3 of internet traffic, provide unprecedented opportunities for understanding and responding to real world situations and challenges. Multimedia Big Data Computing is the new topic that focus on all aspects of distributed computing systems that enable massive scale image and video analytics. During the course of this paper we describe BPEM (Big Picture Event Monitor), a Multimedia Big Data Computing framework that operates over streams of digital photos generated by online communities, and enables monitoring the relationship between real world events and social media user reaction in real-time. As a case example, the paper examines publicly available social media data that relate to the Mobile World Congress 2014 that has been harvested and analyzed using the described system.Peer ReviewedPostprint (author's final draft

Teaching Big Data Management – An Active Learning Approach for Higher Education

Since big data analytics has become an imperative for business success in the digital economy, universities face the challenge to train data scientists and data engineers on various technological and managerial skills. In addition to traditional lectures, active learning formats ensure a practice-oriented education enabling students to handle novel big data technologies. In this paper, we present a big data management syllabus for master students in the field of big data analytics, which includes various hands-on and action learning elements. The course encompasses seven lectures and nine tutorials and takes place at Chemnitz University of Technology. It covers a broad range of big data applications and facilitates knowledge on various cognitive levels. The paper gives an overview of the course content and assigns learning objectives to lectures and tutorials using Krathwohl’s revised taxonomy. Finally, we present the feedback, which we have received by the students over the years

IS Programs Responding to Industry Demands for Data Scientists: A Comparison Between 2011-2016

The term data scientist has only been in common use since 2008, but in 2016 it is considered one of the top careers in the United States. The purpose of this paper is to explore the growth of data science content areas such as analytics, business intelligence, and big data in AACSB Information Systems (IS) programs between 2011 and 2016. A secondary purpose is to analyze the effect of IS programs’ adherence to IS 2010 Model Curriculum Guidelines for undergraduate MIS programs, as well as the impact of IS programs offering an advanced database course in 2011 on data science course offerings in 2016. A majority (60%) of AACSB IS programs added data science-related courses between 2011 and 2016. Results indicate dramatic increases in courses offered in big data analytics (583%), visualization (300%), business data analysis (260%), and business intelligence (236%). ANOVA results also find a significant effect of departments offering advanced database courses in 2011 on new analytics course offerings in 2016. A Chi-Square analysis did not find an effect of IS 2010 Model Curriculum adherence on analytics course offerings in 2016. Implications of our findings for an MIS department’s ability to respond to changing needs of the marketplace and its students are discussed

Supply Chain Management and Big Data Analytics (SCMBDA): Perception to SCM Business

The standards of big data and analytics are being very much buildup with the aid of commercial enterprise executives, media, software program providers and consultant executive. But it is not simply buildup, as some groups are genuinely utilizing big data and analytics in real life experience. Big data and analytics in Supply Chain Management (SCM) has found discovered becoming alluring because of its unpredictability and the extraordinary part. This research proposal highlighting how ?Big Data analytics can be used most productively in managing the supply chain.? They can be utilized to evaluate ?what happened, why it happened, and to develop a plan for change. Based on pre-defined business rules, they can identify where an action is needed, they can help to prepare more accurate forecasts,? and, primarily, they are able to help to determine the best course of motion with WHAT-IF analysis. Materials and Methods used in research proposal describe the promising field of big data analytics in SCM, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the research problem. Possible outcome covered in research proposal how the SCM area can be affected by these new propensities and advancements

IMPACT OF LEARNING ANALYTICS TOWARDS STUDENTS PERFORMANCE

The fast pace of big data analytics advancement makes it necessary for any organization to coincide it with their management and measurement process.  It has become essential for education sectors to analyze this for the development of both learning and academic activities (Shikha. A, 2014). Learning analytics (LA) is the measurement and analysis of the collection of data with regards to learners and their context for making learning more effective. LA is much concern with improving learner’s success. Four dimensions have been identified; data and environment, stakeholders, objectives, and methods. This paper investigates the impact of learning analytics on student’s performance. The focus group was students in Technology Management program at UTM SPACE, Kuala Lumpur. Two research objective has been identified; (i) to find the level of LA understanding among academic staff and (ii) to investigate the relationship between learning analytics and student performance. The research focused on (i) data collection and population at Centre of Diploma Studies, UTM SPACE, KL; (ii) the selected sample will be students in Technology Management’s program; (iii) the research focused on learning analytics with main focus on course assessment reports of core course which are (a) technology management and (b) operation management

Big Data Applications in Digital Marketing

Every year, a set of new trends arise that change the course of the digital marketing process and make it easier for marketers to do their work and save time continuously. One of the most critical new trends that have greatly influenced digital marketing and are expected to sustain its impact in the future is Big Data. This article aimed to outline the role of big data in digital marketing by discussing its various applications in digital marketing operations. This article was based on the systematic review methodology by reviewing the previous literature in the study area. The results obtained from the literature showed various applications of big data analytics in digital marketing, including (improving customer experience, measuring and analyzing competitors, innovation and product development....etc.). The article also discovered that companies regularly employ big data to improve the accuracy of different marketing decisions, such as enhancing customer knowledge, providing highly customized promotional content, increasing sales, and measuring the effectiveness of digital marketing campaigns. This article will provide a theoretical base for future researchers to conduct a field study on Turkish companies to examine to what extent they are using big data analytics in digital marketing

How Do Universities Prepare Students for a Data-driven Business Environment?

As big data becomes a more and more important asset of many organizations, recruiters expect upcoming accounting majors with some data analytics skills. Therefore, many universities actively response the new skill demand and incorporate data analytics in their accounting programs differently. The purpose of this research is to investigate and compare how and to what extent different universities prepare their accounting major students for a data-driven business environment. The scope of the research is limited to the universities that have requested data analytics course syllabus and information from an accounting faculty at Northern Illinois University and some universities within the state of Illinois. There is also limitation that many universities don’t publish the course information in detail on their websites. I firstly searched on the data analytics learning experience expected by AACSB. Then, I investigated how different universities respond to this new learning experience suggestion. According to my sample universities, I collected and prioritized popular data analytics topics, other popular course options, and common use of application software. Based on my finding, universities equip their accounting major students with data analytics skills differently from incorporating small data analytics assignments into traditional accounting courses to offering a data analytics degree.B.S. (Bachelor of Science

Development on advanced technologies – design and development of cloud computing model

Big Data has been created from virtually everything around us at all times. Every digital media interaction generates data, from computer browsing and online retail to iTunes shopping and Facebook likes. This data is captured from multiple sources, with terrifying speed, volume and variety. But in order to extract substantial value from them, one must possess the optimal processing power, the appropriate analysis tools and, of course, the corresponding skills. The range of data collected by businesses today is almost unreal. According to IBM, more than 2.5 times four million data bytes generated per year, while the amount of data generated increases at such an astonishing rate that 90 % of it has been generated in just the last two years. Big Data have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. This paper presents a view of the BD challenges and methods to help to understand the significance of using the Big Data Technologies. This article based on a bibliographic review, on texts published in scientific journals, on relevant research dealing with the big data that have exploded in recent years, as they are increasingly linked to technolog