Rule-Based Cloud System for Performance Appraisal of Staff in Chinese Universities
Yongbin Zhang, Ronghua Liang, Yuansheng Qi, Xiuli Fu, Yanying Zheng · Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) · 2022
Data-driven policies have prevailed among universities globally.Universities hope that the outcomes of faculty members are high quality and in a large quantity.Therefore, universities motivate staff to work hard with annual remuneration bonuses.However, the tedious application process frustrates teachers and brings adverse effects if no suitable systems are available.The paper presents a rule-based cloud computing system to evaluate performance for annual bonus payment applications in Chinese universities.The criteria can be encoded with if-then expressions.Department administrators can add new rules or update existing ones flexibly with this rule-based system.Teachers can upload their achievements through a web browser on different devices.Faculty members know the mark of each item input into the system.The system calculates the final points automatically.Members of the school committee can review and return applications to applicants.All criteria are publicly available for all faculty members.The system simplifies the process of the annual bonus pay application.The transparency encourages teachers to participate in applying for annual bonus pay.The experimental results show that the designed system helped evaluate performancebased annual bonus applications.Teachers spent less time requesting yearly bonus salaries with the rule-based cloud system than with traditional approaches.