Abstract
Over the past 25 years, performance measurement has gained salience in higher education, and with the explosion of structured data and the impact of business analytics and intelligence systems, there are new angles by which big volumes of data can be analyzed. Using traditional analytical approaches, pairs of reciprocal cohorts are considered as two separate discrete entities; therefore, basis of analysis are individual pairs of values, using statistical measures such as average, mean or median, of the total population. Missing in traditional approaches is a holistic performance measure in which the shape of the comparable cohorts is being compared to the overall cohort population (vector-based analysis). The purpose of this research is to examine shape analysis, using a Cosine similarity measure to distil new perspectives on performance measures in higher education. Cosine similarity measures the angle between the two vectors, regardless of the impact of their magnitude. Therefore, the more similar behavior of the two comparing entities can be interpreted as more similar orientation or smaller angle between the two vectors. The efficacy of the proposed method is experimented on the three Colleges of RMIT University from 2011 to 2016, and analyzes the shape of different cohorts. The current research also compared the performance of Cosine similarity with two other distance measures: Euclidean and Manhattan distance. The experimental results, using vector-based techniques, provide new insights to analyzing patterns of student load distribution and provide additional angles by orientation instead of magnitude / volume comparison.
Highlights
Australia’s higher education system has undertaken many successive market-driven reforms since the late 1980s
With respect to the growth of stored structured data in educational organizations, in higher education, the use of modern analytical tools that provide a holistic analysis of student load or headcount data is in increased demand due to competitive forces influencing higher education
The conventional student load analysis on Broad-levels or Education (Blevels) is based on the difference of overall magnitude of Blevels on pairs of years
Summary
Australia’s higher education system has undertaken many successive market-driven reforms since the late 1980s. Conventional student load analysis is basically comparing pairs of reciprocal cohorts which are summarized in the form of “Average” or “Sum” of series of data The essence of such approaches is based on scalar interpretation which focuses on magnitude of results (Ma. Florecilla et al 2017). Two models are proposed in this research: Cosine similarity and Minkowsky distances (Euclidean and Manhattan). These two distance metrics are utilized in image analysis to investigate the similarity between content of images. Calculation of this distance is shown in Formula 2 This analysis provides an alternative lens by which institutional planners can further explain to decision makers’ changes in the student distribution as well as considering its effect on various cohorts. The other critical outcome of this research is that it challenges traditional approaches for examining student load distribution over time, and it suggests new possibilities that can be considered, e.g. where opportunities for growth in certain market segments have been inadvertently missed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
