Big Data, Dashboards, and Data-Driven Educational Decision Making

Big Data, Dashboards, and Data-Driven Educational Decision Making

Todd Price
Copyright: © 2017 |Pages: 12
DOI: 10.4018/978-1-5225-1049-9.ch091
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Business Intelligence (BI) performance dashboards are reliant computer software solutions that enable leaders or companies to gain insight into its critical operations through reporting applications and analysis tools. These management tools gauge performance and progress toward specific operational goals. Fortuitously, educational leaders now have access to dashboards which can be designed and developed to address a wide range of objectives, from monitoring whether online course delivery outcomes are being met by the learner. Conversely, online learners can view their performance with a course dashboard that compares their performance vis-à-vis peers. By merging performance dashboards with online delivery, it is expected that performance goals will be impacted positively. Encyclopedia of Strategic Leadership and Management topics for this chapter include, but are not limited to, the following: Management, the ways CEOs lead, performance management, managing creativity, and decision making and leadership.
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The world is getting smarter. This evolution can be seen everywhere and no industry or sector is immune (Marr, 2015). The information contained within this chapter details a comprehensive process of understanding how data warehouses are vital to the successful implementation of a knowledge information system and visualization technology. Managers of tomorrow need to understand data warehouse design, but they also must possess the skills necessary to relate to the effective and strategic application of these technologies to advance the quality of problem identification and the associated solutions (Marakas, 2003). Consider the following. There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days (Marr, 2015). You have now been introduced to Big Data, a simplistic yet intricate term that most persons underestimate the importance and misunderstanding of said term. However, at this point Big Data shall be shelved in order dig deeper into how to one can make decisions with tools to mine data.

Course interface, usability, testing, data warehousing, and business analytics detail concepts important to the successful implementation of a knowledge information system for higher education. The theory behind the implementation of the process documented within this chapter will hold that online student success will increase as online learning suffers from much higher dropout rates than traditional face-to-face learning. As more data becomes available for the course(s) analyzed through the process of data warehousing and business analytics, abundant variables will be aggregated and analyzed to quantify what efforts must be made to lead to online student success. If practicable and if results produce sound analytics, this project proposal will be conveyed to the college’s Office of Information Technology and the Center for eLearning which functions as support for the Learning Management System provided for online delivery of courses.

For various organizations in our societies to survive and thrive within our society, leaders and managers turn to leadership theories and management science for any needed guidance (Wang). Encyclopedia of Strategic Leadership and Management topics for this chapter focuses on the academia establishment of professional scholars who are engaged in higher education and research where management, the ways managers lead, performance management, managing creativity, and decision making and leadership are probed. As an enterprise grows larger, hundreds of computer applications are needed to support the various business process (Ponniah, 2010).

Before delving into the applications and definitions of decision support systems, management information systems, and learning management systems to support leadership initiatives, let’s take a simple scenario based on an educator’s dilemma of whether learners are meeting course objectives. Although this setting might seem trivial taking into consideration the strategic information, it is at this stage that we can begin to understand the importance of data, misconceptions, and faulty conclusions.

As a full-time Instructional Designer and online Adjunct Instructor at a higher institution for the past five years, the information presented forthwith addresses issues surrounding online learning. First, online learning suffers from much higher drop-out rates than traditional face-to-face learning. Based on a survey completed by more than 200 North American school officials in 2013, Poulin found that course completion rates averaged three to five percent better for on-campus courses than for online courses (Haynie, 2015); thus, retention efforts must be addressed. And second, continuous improvement within course developmental framework provides quality assurance efforts in online learning, which eventually measures learner experiences and outcomes set forth by curriculum development. Now the question begs, where does one start to explore statistics aforementioned and improve performance? The answer lies within data.

Key Terms in this Chapter

Business Intelligence (BI): Raw data transformed into knowledge for decision making.

Data Warehousing: A management system for data storage.

Management Information System (MIS): Information database used for reporting purposes.

Big Data: Exceedingly large volumes of data to be analyzed.

Decision Support System (DSS): Computer based system using data for decision making.

Data Mining: Discovering patterns in Big Data.

Learning Management System (LMS): Online platform for the transfer of knowledge from educator to learner.

Instructional Designer: Developer of online courses based on needs assessments.

Dashboard: Online delivery of data visualization.

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