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Data Mining and Analysis Methodology for Higher Education Curriculum Development and Recruitment Practices: An Interim Report

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Data mining is the process of analyzing data from different perspectives to find new, unique and useful information to aid in decision-making. The reserves of information held by universities in conjunction with data generated from surveys and demographic studies can provide insights to aid curriculum development and recruitment strategies. University administrators and accreditation boards evaluate the success of degree programs, in part, by the success of their graduates. Students succeed through faculty and administration developing coursework and programs that will best prepare them for the work environment. Of concern to university personnel is how faculty and administration can effectively evaluate and modify curriculum and student recruitment to match the dynamic environment of the business world. Universities can learn from private sector business methods to accomplish this. Research being conducted in the Department of Geomatics at the University of Alaska Anchorage (UAA) is intended to explore and implement data analysis and data mining techniques to create a new informational feedback loop to equip faculty for program evaluation and systematic adaptation. As a technologically driven industry, changes in the geospatial workplace are fast. Employers want graduates ready to transition into their world quickly and efficiently. For our graduates to be competitive, the geomatics program must recruit effectively and adapt coursework to match or exceed the rate of change within the geospatial industry. Continually updating curriculum and consistently recruiting the right students will best supply a constantly changing and evolving business environment with the people companies need. The paper is an interim report on the first stages of a data mining project. It shows what can be done with very basic analysis, and also points out a major problem for data mining projects: getting all the data sets into a similar and compatible format that allows deeper analysis.
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Document Type: Research Article

Publication date: September 1, 2012

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