運用資料驅動決策的分析於提升數位化學位論文格式審核工作效率與品質

Using data-driven decision-making analysis to enhance the efficiency and quality of digital thesis and dissertation format review work

本研究旨在探索學位論文格式審核退審原因，提出改善方法，以期降低退審發生率，同時提升數位化學位論文格式審核工作的效率與品質。研究採用Google資料分析流程，包括：「提問」、「準備」、「處理」、「分析」、「分享」，以及「行動」六大步驟，搜集國立成功大學圖書館學位論文審核小組2024年1月1日起至2月7日止退審意見作為分析退審原因的資料來源，歸納出退審主要的原因包括：系統設計、審核者的執行力，以及申請者對相關規定的不熟悉。根據分析結果，本研究提出學位論文上傳系統、審核者、申請者三個面向具體的改善建議，這些建議將有助於管理者做出基於科學方法的資料驅動決策。

This study aims to investigate why thesis formatting reviews are rejected and suggest methods to reduce the rejection rate while improving the quality and efficiency of digital thesis formatting review processes. To achieve this, we utilized the Google data analysis process, which involves six main steps:“Ask”,“Prepare”,“Process”,“Analyze”,“Share”and“Act”. We collected feedback on rejections from the National Cheng Kung University Library’s thesis review team between January 1st and February 7th, 2024, as the data source for analyzing the reasons for rejection. Our analysis identified that the primary reasons for rejection are system design, reviewers’performance, and applicants’lack of familiarity with relevant regulations. Based on the results of our analysis, we proposed specific suggestions for improving the thesis submission system, reviewers, and applicants, which will help managers make data-driven decisions based on scientific methods.