Use of Data Analytics for Program Impact Evaluation and Enhancement of Faculty/Staff Development

Use of Data Analytics for Program Impact Evaluation and Enhancement of Faculty/Staff Development

Samuel Olugbenga King (Auburn University, USA)
DOI: 10.4018/978-1-5225-7598-6.ch035
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This chapter focuses on the use of data analytics to satisfy the accountability demands (summative evaluation) of higher education, while contributing to faculty and staff development in the process (formative evaluation). By situating the data analytics process within a strategic questioning framework, the inquiry has focused on the evaluation of the impact of the programs and services provided by an academic development center at a large research university in the United States. The analytics data, primary findings, have been critiqued and incorporated to enhance further staff and professional development at the center. The findings have also been benchmarked with relevant analytics data from other academic development centers in Europe and Australasia to provide comparative performance measures. The key contribution of the use of data analytics to academic development is its potential to catalyze a data-driven culture that would adequately respond to the 21st century accountability ethos of higher education with systematic, valid, and useful impact/performance measures.
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Background: Institutional Research And Data Analytics

Historically, the field of institutional research in higher education is predominantly focused on the strategic use of information and data sets to implement, review and enhance the academic mission of the university (Buller, 2012). The types of data that have been collected include system-generated behavioral data, e.g., human resource systems; survey data; transactional data, e.g., learning management systems; and frozen data, e.g., admission head counts (Bichsel, 2012, p. 16). But with the technology revolution, higher education is increasingly adopting and leveraging a range of data systems to support institutional capacity and meet strategic goals. The range of data systems that have been deployed to support higher educational units, such as student enrollment, information technology, budgeting and finance, human resources, student success, research administration, and facilities, include enterprise resource management (ERP) systems or business management/intelligence software, academic enterprise systems (e.g., LMS), customer relationship management (CRM) systems, and personalized learning environments, including assessment software (Norris & Baer, 2013, p. 9). An overview of the data analytics ecosystem in higher education is presented in Figure 1.

Figure 1.

Use of data analytics in higher education, with list of providers

Adapted from Norris & Baer, 2013, p. 19.

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