Abstract: Assessment as a dynamic process produces data, which acts as a performance indicator for an individual. The evaluation of instructors’ performance is especially relevant for the academic institutions as it helps to formulate efficient plans to guarantee quality of instructors and learning process. Effort in this work is directed at modeling an intelligent technique for evaluation of instructors’ performance, propose an optimal algorithm and designing a system framework suitable for predicting instructors’ performance The proposed technique will improve reliability and efficiency of instructors’ performance evaluation system, provide basis for performance improvement that will optimize students’ academic outcomes and improve standard of education. Consequently, it will contribute to successful achievement of the goals and objectives defined in the vision and mission of the new education reform agenda. We propose a model to evaluate the performance through the use of techniques of data mining like association, classification rules (Decision Tree, Rule Induction, K-NN, Naïve Bayesian (Kernel)) to determine ways that can help them to better serve the educational process . The data mining methodology used for extracting useful patterns from the institutional database is able to extract certain unidentified trends in teacher’s performance when assessed across several parameters.
Keywords: Educational Institute, Performance Evaluation, Summative Assessment.