基於學生決策結果之校系競爭力分析：以個人申請為例

Abstract

在日趨競爭的高等教育環境下，系所若能明確定位其競爭校系並分析相對競爭力，有助於制定差異化策略，以提升系所競爭力。以數據為基礎的競爭力分析方法，能提供系所因應當前高等教育議題的重要參考。而透過巨量資料分析來協助當前高等教育機構解決所面臨的競爭力問題，更是臺灣高等教育機構以實徵數據分析的科學化及客觀性，做為追求以證據為本的教育決策模式之重要實踐。因此，本研究基於個人申請入學資料（包含申請校系、錄取校系與就讀校系），發展申請者中心之校系競爭力分析，其分析原理與流程如下：首先，轉換申請者同時申請複數校系的紀錄為成對校系相似性（或距離）的測量，再經由集群分析，確認目標系所之競爭集群校系。進一步再透過錄取複數集群內校系的申請者資料，搭配其最後就讀校系的選擇，建立競爭集群校系間的成對強迫選擇反應，並應用羅氏自比模型（為強迫選擇模型之一）估計個別校系的潛在競爭力。研究結果指出，集群分析能有效定位系所競爭集群，而羅氏自比模型分析則能精確進行集群內校系相對競爭力之估計。對於當前的校務研究議題，例如：辦學績效檢驗、校系競爭力分析，以及系所定位與未來發展方向等，本研究成果有助於提供問題解決之線索，並引領後續研究方向。In the increasingly competitive higher education environment, a department has to develop differentiation strategies among its competitor group to improve its own competitiveness. To this end, the information about the competing departments and its relative competitiveness is important. Accordingly, a data-based competitive analysis can help to provide references for the department to respond to the current higher educational issues. Utilizing big data analysis to assist higher education institutions in solving the encountered competition problems can be considered a demonstration that uses scientific and objective practical data analysis as the evidence-based educational decision- making process for higher education institutions in Taiwan. Therefore, this study conducted a series of applicant-centered analyses of departments' competitiveness based on individual application data. The general principle and process are as follows: First, the applicant's record of applying for multiple departments is transformed into the measures of similarity (or distance) between each pair of the applied departments, and the similarity measures are then used to identify the cluster of competitive departments for the target department through the cluster analysis. Next, based on the information of applicants who were accepted by multiple within-cluster departments and attended one of them, the response matrix of paired forced-choice responses between those departments is generated. The Rasch ipsative model (RIM), one of forced-choice models, was then applied to estimate the parameters of ＂potential competitiveness＂ for the departments based on the response matrix. The results indicated that cluster analysis can effectively locate the clusters of competing departments, while the application of the RIM could precisely estimate the relative competitiveness of within-cluster departments. The study can provide important references and guide the direction for further research for current institutional research issues, including the institution performance evaluation, the department competitive analysis, and the self-positioning and future development direction for the institution.