A big data approach to assessing the US higher education service

Abstract

There are a number of ranking systems to provide assessment services on higher education regionally, nationally, or internationally. Note that the subjective evaluation index and indicator inclusions and weights that are usually applied in current ranking systems. As a result, the question of the objectivity and impartiality of the provided rankings arises. One of our studies addressed these concerns by applying a quantitative and model-driven approach to acquiring the evaluation index and factor weights, which was successfully validated in the US News & World Report ranking system [1]. To extend our earlier study, this paper further shows a very interesting result by developing a real-time, scalable, and model-driven higher education ranking system with the support of big data technologies. This extended study reveals promising potential in enhancing varieties of applications across the service industry.