Work place: Department of Computing, The Hong Kong Polytechnic University, Hong Kong
E-mail: qy.li@connect.polyu.hk
Website:
Research Interests: Visualization, Computer systems and computational processes, Pattern Recognition, Data Mining, Data Structures and Algorithms
Biography
Mr. Qingyun Li received the B.Sc. and M.Sc degree in 2003 and 2006 from Northeastern University, Shenyang, and P.R. China. He is currently pursuing the Ph.D. degree with The Hong Kong Polytechnic University. He is also currently working as Computer Officer with The Hong Kong Institute of Education. His research interest focuses on pattern recognition, sequential pattern mining, context mining and big data visualization.
By Qing-Yun Li
DOI: https://doi.org/10.5815/ijmecs.2015.03.01, Pub. Date: 8 Mar. 2015
This paper analyzed 8 years’ student admission trend of Hong Kong Institute of Education (HKIEd) postgraduate programmes in 2 time slots. In the first time period, year 2007-2010, we use the waterfall and competing pattern model for the quantitative analysis of the postgraduate programmes in a holistic manner of HKIEd, and further study the general student admission trend of year 2011-2013, due to the sharp increase of mainland China students and the 3-3-4 Scheme Education System Reform in 2011 and 2012. In this paper, we utilize this rich collection of programmes to identify usable admission patterns by analyzing admission data of the past years for reinforcing the institute’s strength and reputation, whose programme spectrum was expanded and enhanced quite drastically since 2005 with the introduction of 1st Master in Education cohort. In particular, this paper is focused to locate: 1) if there is any internal competition and how severe the competition is. 2) If there is any programme that can be utilized as strategic recruitment? and 3) the general student admission trend of the 8 years. With this study, management can better understand where the sources of applicants come from. New insight can be drawn and new strategy can be defined to recruit more quality students which are also aligned with our strategic move to transform the students. The methods used in this paper can be easily applied in other University for analyzing the admission issues, and can demonstrate to management how operation data can be utilized to form valuable insights and significant improvement can then be made based on these identified facts.
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