Academic journal article Journal of Information Systems Education

Teaching Social Media Analytics: An Assessment Based on Natural Disaster Postings

Academic journal article Journal of Information Systems Education

Teaching Social Media Analytics: An Assessment Based on Natural Disaster Postings

Article excerpt


Recent interest in "big data" analytics has escalated the demand for data analytics specialists. According to Deloitte (2012) there will be both a strong demand for skilled big data professionals in the US over the next five years and a shortage of 200,000 IT professionals with deep analytics skills (Manyika et al., 2011). Chiang, Goes, and Stohr (2012) recently suggested that business intelligence and analytics education program development provides a unique opportunity for the information systems discipline. They contended that the IS discipline should address the challenges of big data--especially in business school IS programs--to meet the growing demand for graduates who can aggregate, analyze, model and evaluate organizational data. To meet this challenge, schools are expanding business programs to develop IS graduates with the business, analytics, IT, and communications skills required for successful future business analytics leaders (Henschen, 2013; NUS-SOC, 2013). Chiang, Goes, and Stohr (2012) suggested that since the IS field has traditionally focused on quantitative, structured data, there is a need to address the interpretive aspect of analytics.

The increasingly popular social media applications such as Twitter, Facebook, blogs, and online product reviews lie within the "big data" spectrum and contain relevant information for business decision making. A survey by Wixom et al. (2014) noted the increasing interest in text analysis to extract information from semi-structured and non-structured data. Natural language processing and semantic interpretation are increasingly popular analytics solutions (Gorman & Klimberg, 2014). As technology evolves, undergraduate IS Business Intelligence and Analytics (BI&A) curriculum should include social media analytics (Topi et al., 2010).

In light of the above, we aim to demonstrate an assessment structure for teaching social media analytics concepts with the goal of analyzing and interpreting social media content. The proposed assessment supports the sharing of reusable teaching resources from different social media content for teaching social analytics in the IS curriculum. We have organized the remainder of this paper as follows. First we review the literature on business analytics teaching and the literature on analytics frameworks for assessment design. We then discuss our methodology for data collection and pre-processing, after which we present the learning enquiries and results. Next we discuss our findings. We conclude with our solutions, the limitations of this study, and suggestions for future studies.


2.1 Teaching Business Analytics

While new business analytics programs have multiplied dramatically since 2010, teaching resources such as datasets and case studies remain scarce for professors in this field (Wixom et al., 2014). A Pro-Quest database search of the literature for teaching cases and learning issues relating to business and data analytics in the IS discipline yielded few relevant results. There are not many IS publications on research regarding business analytics teaching and learning. Marchand and Peppard (2013) recommend that teachers put more effort into producing teaching resources and cases that focus on how people create and use information, and how to frame questions that data analytics might answer to increase our knowledge and understanding. Technology is changing rapidly, stressing the need for sharing and reusing innovative business analytics teaching practices and resources to resolve challenges concerning the core body of knowledge and the design and delivery of BI&A programs (Marjanovic, 2013).

In the IS discipline, BI&A includes three evolving categories: BI&A 1.0, 2.0 and 3.0. BI&A 1.0 comprises Database Management System transaction based structured content such as credit card and purchase transaction data. BI&A 2. …

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