總效率極大化之教師專長分類

大學教師的績效管理，已蔚為各高等教育學校未來品質發展的重要關鍵。探討如何將教師專業績效分類，期能使得全校教師總體績效為最佳，是為本研究主要目的。本研究，依照學校之期望，按教師各類專長遴選分類，依學校指定各分類之總人數為目標，應用資料包絡分析之CCR模式，將教師在「研究」、「教學」及「服務」等專業領域分類，其中未能完全分類者，再輔以0-1規劃及變項變異最小方式分類，完成全校之教師分類。為說明本研究之精神及其間之功效，提出一個引理，輔助說明。為了說明本研究對於教師專長分類之合理性，將研究之教師三種專長分類以CCR專家權重設限效率值，實施單因子變異數分析，P=0.9975>.05，各組之間無顯著差異，顯見三種分類變量非常均勻，即達分類均等條件；為檢視以CCR模式分類達整體效率之最高，以隨機抽樣視為傳統計量分類，再將CCR分類與傳統分類兩種分類方式所得之平均效率值比較結果，CCR模式分類所得研究、教學、服務各類的平均效率值，皆大於傳統分類之平均效率值。

關鍵字

CCR模式；專家權重法； 0-1規劃；單因子變異數分析

並列摘要

The performance management of university teachers has played an important role in the future quality development of the university. This research aims to explore how to classify teachers' professional performance in order to optimize the overall performance of university teachers. This study indicated that the university will classify personnel based on its long-term plan and teachers' specialties when selecting teachers. The DEA-CCR model was adopted in this study and the university teachers were classified by their expertise such as research, teaching, and services. For teachers who cannot be classified by these rules, the 0-1 programming and the least variable variation classification were employed to complement and complete the classification of university teachers. A lemma was proposed to provide additional explanation to illustrate the spirit and effectiveness of this study. To demonstrate the rationality of classifying teachers’ specialties, this research applied the CCR-based expert-weighting method to set restrictions on the efficiency values of the three specialties (research, teaching, and service). The results of the one-way ANOVA analysis, P=0.9975>0.05 show that there were no significant differences between the groups. It is obvious that the variables of the three categories are quite even, which means these categories are rendered equal. To examine whether the CCR-based classification method can obtain the highest efficiency value, this research adopted a random sampling for traditional classification and CCR-based classification. Afterward, the mean of the efficiency values obtained via the two methods was compared. The findings indicate the average efficiency values of the three specialties (research, teaching, and service) obtained through the CCR-based classification are all greater than the average efficiency value obtained based on traditional classification.