چکیده (انگلیسی):

Lecturer performance analysis has enormous influence on the
educational life of lecturers in universities. The existing system in
universities in Kurdistan-Iraq is conducted conventionally, what is more,
the evaluation process of performance analysis of lecturers is assessed by
the managers at various branches at the university andin view of that, in
some cases the outcomes of this process cause a low level of endorsement
among staffs who believe that most of these cases are opinionated. This
paper suggests a smart and an activesystem in which both unique and
multiple soft computing classifier techniques are used to examine
performance analysis of lecturers of college of engineering at Salahaddin
University-Erbil (SUE). The dataset collected from the quality
assurancedepartment at SUE. The dataset composes of three sub-datasets
namely: Student Feedback (FB), Continuous Academic Development
(CAD) and lecturer’s portfolio (PRF). Each of the mentioned subdatasets
is classified with a different classifier technique. FB uses Back-
Propagation Neural Network (BPNN), CAD uses Naïve Bayes Classifier
(NBC) and the third sub-dataset uses Support Vector Machine (SVM) as
a classifier technique. After implementing the system, the results of the
above sub-datasets are collected and then fed as input data to BPNN
technique to obtain the final result and accordingly, the lectures are
awarded, warned or punished.