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ThinkMind // ACCSE 2017, The Second International Conference on Advances in Computation, Communications and Services // View article accse_2017_4_30_88003


TOPSIS Assisted Selections of the Best Suited Universities for College Applications in Mainland China

Authors:
Shan Lu
Jie Wang

Keywords: TOPSIS; weighted criteria; multi-attribute decision making; recommendation system

Abstract:
College admissions in mainland China depend mainly on the scores of the standardized annual examination called Gaokao. Students submit a common application to their provin- cial Gaokao office, on which they are allowed to list a fixed and small number of universities and majors they intend to study. The admission process in a province follows one of the following three admission models: parallel, gradient, and a combination of both. No matter what admission models are used, there is always a possibility that an applicant could end up being rejected by every university listed in the application, even though the applicant could have been accepted by a university not in the list. This process presents a challenge for students to figure out how to select universities to apply so that they can be admitted by a university and major that match their abilities and interests. To many students, and their parents, this is a difficult decision to make and their experience is unpleasant. To help reduce this agony, we present a new approach of applying Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to generate a personalized selection of the best suited universities and majors that match a student’s Gaokao score and meet a list of criteria. We then present case studies to demonstrate the effectiveness of this approach.

Pages: 54 to 59

Copyright: Copyright (c) IARIA, 2017

Publication date: June 25, 2017

Published in: conference

ISSN: 2519-8459

ISBN: 978-1-61208-570-8

Location: Venice, Italy

Dates: from June 25, 2017 to June 29, 2017

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