Teaching Social Media Analytics: An Assessment Based on Natural Disaster Postings
Teaching Social Media Analytics: An Assessment Based on Natural Disaster Postings
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社交媒体分析教学：基于自然灾害帖子的评估
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Pei
中科院分区：
文献类型：
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作者：
T. Goh;Pei
1.简介最近人们对“大数据”分析的兴趣提升了对数据分析专家的需求。根据德勤(2012)的预测，未来五年，美国对熟练的大数据专业人员的需求将十分旺盛，而具有深厚分析技能的IT专业人员将短缺200,000人(Manyika等人，2011年)。Chiang、Go和Stohr(2012)最近提出，商业智能和分析教育项目的发展为信息系统学科提供了一个独特的机会。他们认为，信息系统学科应该应对大数据的挑战--特别是在商学院的信息系统项目中--以满足对能够聚合、分析、建模和评估组织数据的毕业生日益增长的需求。为了应对这一挑战，学校正在扩大商业项目，以培养具有商业、分析、IT和沟通技能的信息系统毕业生，这些技能是未来成功的商业分析领导者所需的(Henschen，2013；NUS-SOC，2013)。Chiang，Go和Stohr(2012)建议，由于信息系统领域传统上关注的是量化的、结构化的数据，因此有必要解决分析的解释性方面。越来越流行的社交媒体应用程序，如Twitter、Facebook、博客和在线产品评论，属于“大数据”范畴，包含用于商业决策的相关信息。Wixom等人的一项调查。(2014)注意到人们对文本分析越来越感兴趣，以便从半结构化和非结构化数据中提取信息。自然语言处理和语义解释是日益流行的分析解决方案(Gorman&Klimberg，2014)。随着技术的发展，本科生的商业智能与分析(BI&A)课程应该包括社交媒体分析(Topi等人，2010)。有鉴于此，我们的目标是展示一种用于教授社交媒体分析概念的评估结构，目的是分析和解释社交媒体内容。拟议的评估支持共享来自不同社交媒体内容的可重复使用的教学资源，用于在信息系统课程中教授社会分析。我们对本论文的其余部分进行了如下组织。首先，我们回顾了有关商业分析教学的文献和关于评估设计的分析框架的文献。然后，我们讨论了我们的数据收集和预处理的方法，然后我们提出了学习询问和结果。接下来，我们讨论我们的发现。最后，我们总结了我们的解决方案，本研究的局限性，并对未来的研究提出了建议。2.文献回顾2.1教授商业分析虽然新的商业分析课程自2010年以来急剧增加，但该领域的教授仍然缺乏数据集和案例研究等教学资源(Wixom等人，2014年)。在Pro-Quest数据库中搜索与IS学科中与业务和数据分析有关的教学案例和学习问题的文献，结果很少。关于商业分析教与学的研究的IS出版物并不多。Marchand and Peppard(2013)建议教师花更多的精力制作教学资源和案例，重点关注人们如何创造和使用信息，以及如何构建数据分析可能回答的问题，以增加我们的知识和理解。技术日新月异，强调了分享和重用创新的商业分析教学实践和资源的必要性，以解决与核心知识体系以及BI&A项目的设计和交付相关的挑战(Marjanovic，2013)。在信息系统学科中，BI&A包括三个不断发展的类别：BI&A 1.0、2.0和3.0。BI&A 1.0包括基于数据库管理系统交易的结构化内容，如信用卡和购买交易数据。BI&A 2.…
1. INTRODUCTION 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. LITERATURE REVIEW 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. …