Comparative Analysis of Decision Support Models for Faculty Performance Evaluation

The faculty is an important asset to guarantee that an academic institution operates as expected. Performance evaluation is an important tool used to assess faculty efficiency in the workplace. The study focuses on the comparison of three different decision support models identifying the suited model to be used in the proposed faculty performance evaluation framework. A local community college provided the historical data and documents to the researchers. The researcher selected three suitable decision support models and used Weka for data analysis. The results of preliminary data analysis examined shows that the identified faculty performance evaluation criterion includes 75% of the National Budget Circular (NBC) criteria; 15% IPCR and 10% College Involvement and Participation (CIP). The comparative analysis criteria used in analyzing the decision tree would be utilized as the model in the knowledge-based decision support system. With regards to build time, both Random Tree and REP Tree resulted in 0 seconds while M5P has 0.23 seconds. Build time would affect the model efficiency in terms of resources needed for execution. REP Tree has the highest size of tree produced in the model. Since all the decision tree models have positive coefficients, it indicates that when the value of one variable increases, the value of the other variable also tends to increase. The results of comparing the decision support models in this study had identified potential suitability of a model in faculty performance evaluation. Furthermore, policies in the locale could be based on the logical decision trees presented in this study.