Academic Analytics

Academic Analytics

Si Chen (Murray State University, USA)
Copyright: © 2014 |Pages: 7
DOI: 10.4018/978-1-4666-5202-6.ch004
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Main Focus

Academic analytics can be used in different areas in higher education. Antons et al., 2006 documents a successful application of data-mining techniques in enrollment management through a partnership between the admissions office, a business administration master's-degree program, and the institutional research office. Blanc et al., 2009 analyzes a large sample of 33,000 university alumni records and clusters them into six groups which are evaluated using discriminant analysis for different giving patterns. Nandeshwar et al., 2009 develops a decision tree model to explain admissions data at West Virginia University and finds that financial aids is the leading factor to attract students to enroll. As the demand for institutional accountability continues to rise, analytics for the purpose of improving student success has emerged as the main focus of academic analytics.

Student Success

Student success is often measured by student retention or student graduation rate. It has always been a major concern in the higher education community. Indeed, our universities’ ability to produce high quality graduates plays a key role in the future of the nation as we confront the challenges of globalization. Additionally, it affects the amount of state funding received by public universities and the eligibility of for-profit schools and certificate programs at public schools for federal student loans.

Key Terms in this Chapter

Real-time Analytics: The use of, or the capacity to use, all available enterprise data and resources when they are needed. It consists of dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use.

Enterprise Resource Planning Systems: A system comprised of a single or integrated suite of applications to manage enterprise business functions, including finance, human resources, and order fulfillment.

Data Mining: The process of using sophisticated data search capabilities and statistical algorithms to discover patterns in large data sets. Data mining techniques commonly used in academic analytics include regression analysis, rule induction, clustering, neural networks, and decision trees.

Course Management Systems: A software application for the administration, documentation, tracking, reporting and delivery of education courses or training programs. One of the most popular course management systems is Blackboard.

Predictive Modeling: The process of creating models that can be used to predict a decision, a ranking or an estimate.

Academic Analytics: The broad applications of both business intelligence, which traditionally focuses on query and reporting, as well as predictive modeling and data mining techniques for decision making in higher education.

Student Information Systems: The institutional database(s) controlling student records, information, courses, and grades. It is often part of a larger enterprise resource planning system that includes billing, payment, payroll, and other administrative functions.

Student Success: The achievement of the student’s own, often developing, education goals. It is often measured by student retention or student graduation rate at the institutional level.

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