Exploring new angles to analyze student load data

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.