Vector-based Models for Educational Institution Shape Analysis

Rouhi, A 2017, 'Vector-based Models for Educational Institution Shape Analysis', in Proceedings of the 17th Annual SEAAIR Conference , Singapore, 6-8 September 2017, pp. 1-1.


Document type: Conference Paper
Collection: Conference Papers

Title Vector-based Models for Educational Institution Shape Analysis
Author(s) Rouhi, A
Year 2017
Conference name 17th Annual SEAAIR Conference 2017
Conference location Singapore
Conference dates 6-8 September 2017
Proceedings title Proceedings of the 17th Annual SEAAIR Conference 
Publisher SEAAIR
Place of publication Singapore
Start page 1
End page 1
Total pages 1
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, sum, mean or median, of the total population. Missing in traditional approaches is the lack of 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, i.e. load pattern distribution, between the two vectors. The efficacy of the proposed method is experimented on a college of RMIT University from 2010 to 2016. The current research also proposed two other distance measures: Euclidean and Manhattan distances. The experimental results provide new insights to analyzing patterns of student load distribution and provide additional angles by orientation instead of magnitude / volume comparison. These insights assist University executive to be assured of the decision making process.
Subjects Higher Education
Keyword(s) Load Pattern Distribution
Vector-based Analysis
Shape Analysis
Cosine Similarity
Magnitude versus Orientation.
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Created: Wed, 19 Sep 2018, 13:35:00 EST by Catalyst Administrator
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